Thesis
Reference
Aspects for the Swiss Energy Transition: Policy and energy efficiency measures for buildings
CHO, Haein
Abstract
Switzerland have adopted ambitious policy targets for renewable energy and energy efficiency. Although diverse range of policy instruments and energy efficiency measures have been adopted, a deeper understanding is required for practical implementation of the measures. The research mainly applied case study-oriented approach. By monitoring the outcome of the selected energy efficiency measures for heating (hydraulic balancing) and ventilation (hybrid ventilation system) system in existing buildings, the thesis measured the actual impact of the applied measures in buildings, assessed the achieved energy efficiency and developed concrete recommendations considering occupant behavior. By assessing the impact of utility-run energy efficiency programs (EEPs) on electricity savings in Switzerland and the United States, the research explores best operational practices that could be adopted in Switzerland. The thesis contributes to establishing practical insights into how to operate and evaluate the impact of EEPs and energy efficiency measures to catalyze a sustainable energy transition in Switzerland.
CHO, Haein. Aspects for the Swiss Energy Transition: Policy and energy efficiency measures for buildings. Thèse de doctorat : Univ. Genève, 2020, no. Sc. 5530
URN : urn:nbn:ch:unige-1498002
DOI : 10.13097/archive-ouverte/unige:149800
Available at:
http://archive-ouverte.unige.ch/unige:149800
Disclaimer: layout of this document may differ from the published version.
UNIVERSITÉ DE GENÈVE FACULTÉ DES SCIENCES Section des Sciences de la Terre et de l’Environnement Professeur Martin K. Patel Département F.-A. Forel
des Sciences de l’Environnement et de l’Eau Institut des Sciences de l’Environnement
Aspects for the Swiss Energy Transition – Policy and energy efficiency measures for buildings
THÈSE
présentée à la Faculté des sciences de l’Université de Genève pour obtenir le grade de Docteur ès sciences, mention sciences de l’environnement
par
Haein Cho
de
Busan (Corée du sud)
Thèse N 5530
GENÈVE
Repromail – Université de Genève
2020
Acknowledgements
I would like to express my sincere gratitude to my thesis supervisor, Prof. Dr. Martin Patel not only for his continuous support but also for sharing his knowledge and skills. Discussions with him have greatly widened my knowledge and provided me with new insights for my research. I would also like to thank the members of this thesis committee Prof. Dr. Stefano Schiavon, Prof. Dr. Jean-Philippe Bacher, Dr. Christian Rod, Dr. Pierre Hollmuller and Dr.
Kapil Narula, supervisor of my pre-doc exam Dr. Edward Vine and my colleagues Daniel Cabrera and Dr. Meinrad Bürer for their time, support, and good will to share their expertise and scientific feedbacks.
I also express appreciation to my colleagues from University of Geneva who have provided me with assistance and advice, Eric Pampaloni, Fleury de Oliveira, Dr. Stefan Schneider and Dr. Carolina Fraga.
Many thanks to my colleagues from Energy Efficiency group and my dear friends for making my Ph.D. journey more colorful and vivid.
Last but not least, I would like to thank my family for their support and understanding during the entire period of my thesis.
Abstract
Recognizing renewable energy and energy efficiency as key elements to achieve this transition, countries including Switzerland have adopted ambitious policy targets and timelines. Since energy efficiency is considered as the most cost-efficient solution both for short- and mid-term, it has been targeted in various sectors and particularly for buildings due to its high share of total final energy consumption in Switzerland. Although diverse range of policy instruments and energy efficiency measures have been adopted in buildings to harvest maximum energy savings potential, a deeper understanding is required of the measures that can be implemented today.
To provide a practical assessment under real-life constraints, the research mainly applied case study oriented approach. By monitoring the outcome of the selected energy efficiency measures for heating (hydraulic balancing) and ventilation (hybrid ventilation system) system in existing buildings, the thesis measured the actual impact of the applied measures in buildings, assessed the achieved energy efficiency and developed concrete
recommendations. Unlike earlier studies, which did not consider the impact of occupant behavior on energy consumption in buildings, this thesis takes into account occupant behavior through field monitoring and scenario simulations. The energy savings are predominantly delivered through energy efficiency programs (EEPs), which is one type of policy instrument for improving energy efficiency. By assessing the impact of EEPs on electricity savings in Switzerland and the United States, the research explores best operational practices that could be adopted in Switzerland.
Integrating Fanger’s model into simulations of energy use in buildings, the research estimated the comfort and behavior-related temperature range and established the associated energy saving potentials to lie in a range between 2 and 14%. The estimated range from simulations was corroborated by a case study using data from existing buildings.
By monitoring a group of identical multifamily buildings in Geneva among which half
implemented hydraulic balancing (treatment group) and the other half did not (control group), the research found that 9% of energy can be saved annually from hydraulic balancing.
Applying the regularization model, the study concluded that practitioners should target
capacity to harvest maximum energy savings from hydraulic balancing. The hybrid ventilation system installed in multifamily buildings in Geneva achieved a good level of indoor air quality consuming 90% less electricity than mechanical ventilation with heat recovery (MVHR).
However, by recovering the lost heat, the total primary energy usage of the MVHR is 5% less than the hybrid system. Occupant behavior to open windows was found to be responsive to changes in indoor CO2 concentration and indoor temperature. This indicates that the two parameters could be integrated in the design of ventilation strategy. The EEPs in Geneva are found to have saved 1kWh of electric energy with an investment of 8 cents in the residential sector while the 11 leading states in the US demonstrated 1kWh was saved using 4 cents on an average. This was mainly due to economies of scale achieved in the US by bundling measures and through knowledge and technology spillovers, which enabled them to distribute the administrative costs across large number of programs and participants. The difference in cost-effectiveness in Geneva and in the US was found to be much lower for the commercial and industrial sector (3.6 cents/kWh for Geneva vs 2.2 cents/kWh in the US).
The thesis contributes to establishing practical insights into how to operate and evaluate the impact of EEPs and energy efficiency measures. The findings could help other Swiss cantons and other bodies and organisations in Switzerland and abroad to examine the compatibility of the studied measures with the respective local context. Incorporation of local knowledge at regional and national scale will result in economies of scale promoting cost effective solutions for energy savings which will catalyze a sustainable energy transition.
Résumé
Reconnaissant que les énergies renouvelables et l'efficacité énergétique sont des éléments clés pour réaliser cette transition énergétique, des pays comme la Suisse ont adopté des objectifs politiques et des calendriers ambitieux. L'efficacité énergétique étant considérée comme la solution la plus rentable à court et à moyen terme, elle a été ciblée dans divers secteurs et en particulier pour les bâtiments en raison de leur part élevée dans la
consommation totale d'énergie finale en Suisse. Bien que divers instruments politiques et mesures d'efficacité énergétique aient été adoptés dans les bâtiments afin d'exploiter au maximum le potentiel d'économies d'énergie, il est nécessaire de mieux comprendre les mesures qui peuvent être mises en œuvre aujourd'hui.
Afin de fournir une évaluation pratique sous des contraintes réelles, la recherche a appliqué principalement une approche axée vers des études de cas. En suivant les résultats des mesures d'efficacité énergétique sélectionnées pour le chauffage (équilibrage hydraulique) et la ventilation (système de ventilation hybride) dans les bâtiments existants, la thèse a permis de mesurer l'impact réel des mesures appliquées dans les bâtiments, a évalué l'efficacité énergétique atteinte et a élaboré des recommandations concrètes. Contrairement aux études précédentes, qui ne prenaient pas en compte l'impact du comportement des occupants sur la consommation énergétique dans les bâtiments, cette thèse prend en compte le
comportement adaptatif des occupants grâce à un suivi sur le terrain et à des simulations de scénarios. Les économies d'énergie sont principalement réalisées grâce aux programmes d'efficacité énergétique (PEE), qui constituent un type d'instrument politique pour améliorer l'efficacité énergétique. En évaluant l'impact des PEE sur les économies d'électricité en Suisse et aux États-Unis, la recherche explore les meilleures pratiques opérationnelles qui pourraient être adoptées en Suisse.
En intégrant le modèle de Fanger dans les simulations de l'utilisation de l'énergie dans les bâtiments, la recherche a estimé la plage de température liée au confort et au comportement et a établi que les potentiels d'économie d'énergie associés se situent dans une plage
comprise entre 2 et 14%. La fourchette estimée à partir des simulations a été corroborée par une étude de cas utilisant des données provenant de bâtiments existants. En surveillant un
équilibrage hydraulique (groupe de traitement) et l'autre moitié non (groupe de contrôle), la recherche a constaté que 9% de l'énergie peut être économisée annuellement grâce à l'équilibrage hydraulique. En appliquant le modèle de régularisation, l'étude a conclu que les professionnels devraient cibler les bâtiments qui ont été construits avant 1980, qui ont de grandes surfaces chauffées (la surface de référence énergétique des bâtiments, SRE, en Suisse) et une chaudière de grande capacité pour réaliser un maximum d'économies d'énergie grâce à l'équilibrage hydraulique. Le système de ventilation hybride installé dans les bâtiments multifamiliaux à Genève a permis d'atteindre un bon niveau de qualité de l'air intérieur en consommant 90% d'électricité en moins que la ventilation mécanique contrôlée double flux (VMC double flux). Cependant, en récupérant la chaleur perdue, la
consommation totale d'énergie primaire de la VMC double flux est inférieure de 5% à celle du système hybride. Le comportement des occupants concernant l'ouverture des fenêtres s'est avéré sensible aux changements de la concentration en CO2 et de la température intérieure. Cela indique que ces deux paramètres pourraient être intégrés dans la conception de la stratégie de ventilation. Les PEE de Genève ont permis d'économiser 1 kWh d'énergie électrique avec un investissement de 8 cents (dollar américain) dans le secteur résidentiel, tandis que les 11 principaux États américains ont démontré qu'un kWh était économisé en utilisant 4 cents en moyenne. Cela est principalement dû aux économies d'échelle réalisées aux États-Unis par le regroupement des mesures et par les retombées en matière de connaissances et de technologies qui leur ont permis de répartir les coûts administratifs sur un grand nombre de programmes et de participants. Les différences de rentabilité entre Genève et les États-Unis se sont avérées beaucoup plus faibles pour le secteur commercial et industriel (3,6 cents/kWh à Genève contre 2,2 cents/kWh aux États-Unis)
La thèse contribue à établir des aperçus pratiques sur la façon de faire fonctionner et d'évaluer l'impact des PEE et des mesures d'efficacité énergétique. Les résultats pourraient aider d'autres cantons suisses, d'autres organismes et organisations en Suisse et à
l'étranger à examiner la compatibilité des mesures étudiées avec le contexte local respectif.
L'intégration des connaissances locales à l'échelle régionale et nationale permettra de réaliser des économies d'échelle en promouvant des solutions rentables pour les économies d'énergie, ce qui catalysera une transition énergétique durable.
Table of content
Acknowledgements ... i
Abstract ... ii
Résumé ... iv
Abbreviations ... ix
Introduction ... 1
Swiss climate and energy strategy ... 2
Energy use and CO2 emissions by sector in Switzerland ... 2
Policy instruments and measures ... 3
1.3.1 CO2 levy ... 3
1.3.2 CO2 levy reimbursement and grid surcharge ... 3
1.3.3 EE tenders for more efficient electricity use... 4
1.3.4 Building program ... 4
1.3.5 Electric appliances ... 4
1.3.6 Policies across different levels of government ... 5
1.3.7 Discussion of policy measures... 6
Scope of research ... 6
Estimation of energy savings potential through hydronic balancing - A case study in Geneva, Switzerland ... 10
Introduction... 11
Methodology ... 12
2.2.1 Data collection ... 14
2.2.2 Building characteristics ... 15
2.2.3 Processing of temperature data ... 16
2.2.4 Thermal Sensation Model ... 17
2.2.5 Energy savings potential for space heating ... 18
2.2.6 Application of methodology... 19
Results ... 19
2.3.1 Indoor temperature ... 19
2.3.3 Analysis of thermal comfort and energy savings potential ... 28
Discussion ... 35
Conclusion ... 36
Appendix ... 38
Identification of criteria for the selection of buildings with elevated energy saving potentials from hydraulic balancing - Methodology and case study ... 39
Introduction... 40
Methodology ... 42
3.2.1 Part I: Identification of key variables ... 43
3.2.2 Part II: Estimation of energy savings through case study ... 45
Results and Discussions ... 46
3.3.1 Part I: Analysis of key variables and interations ... 46
3.3.2 Part II: Results of case study ... 50
Limitations and future research ... 57
Conclusions... 57
Evaluation of performance of energy efficient hybrid ventilation system and analysis of occupants’ behavior to control windows ... 59
Introduction... 60
Data collection ... 62
4.2.1 Field monitoring ... 62
4.2.2 Characteristics of the ventilation system installed ... 63
4.2.3 Ventilation control strategy ... 64
Methods ... 65
4.3.1 Independent variables ... 65
4.3.2 Exploratory data analysis ... 65
4.3.3 Model formulation: Generalized Additive Model (GAM) ... 65
Results ... 66
4.4.1 Evaluation of performance of hybrid ventilation system ... 66
4.4.2 Modeling results of occupant behavior to open windows ... 72
Discussion ... 77
4.5.1 Methods for modelling windows opening ... 77
4.5.2 Practical implications ... 77
4.5.3 Limitations of this study ... 79
Conclusion ... 80
Appendix ... 81
Comparative analysis of customer-funded energy efficiency programs in the United States and
Switzerland - Cost-effectiveness and discussion of operational practices... 83
Introduction... 84
5.1.1 Policy background in the US, Europe and Switzerland ... 84
5.1.2 Comparison of energy efficiency objectives and strategies in Switzerland and the US 85 5.1.3 Selection of exemplary cases for electric energy efficiency programs ... 86
5.1.4 Comparison of local contexts among selected cases... 87
Method ... 90
Results and discussion ... 95
5.3.1 Assessment of energy efficiency portfolio expenditure ... 95
5.3.2 Analysis of levelized cost of saved energy (Residential and Commercial & Industrial sectors) 102 5.3.3 Low-income programs ... 105
5.3.4 Comparison with cost analysis results from other studies ... 109
Conclusions and policy implications ... 111
Appendix ... 113
Conclusion and recommendations ... 115
Summary ... 115
6.1.1 Summary of Chapter 2. Estimation of energy savings potential through hydraulic balancing of heating systems in buildings... 116
6.1.2 Summary of Chapter 3. Identification of criteria for the selection of buildings with elevated energy saving potentials from hydraulic balancing - Methodology and case study .... 116
6.1.3 Summary of Chapter 4. Evaluation of performance of energy efficient hybrid ventilation system and analysis of occupant behavior to control windows ... 117
6.1.4 Summary of Chapter 5. Comparative analysis of customer-funded energy efficiency programs in the United States and Switzerland ... 119
Conclusion ... 120
Recommendations for Future studies... 122
List of figures ... 124
List of tables ... 127
Bibliography ... 129
CV and publication list ... 151
Abbreviations
ACEEE American Council for an Energy Efficient Economy AIC Akaike information criterion
ARRA American Recovery and Reinvestment Act BIC Bayesian information criterion
CAP Community Action Partnership
CECB Certificat Énergétique Cantonal des Bâtiments CEE Consortium Energy Efficiency
DPCVs Differential Pressure Control Valves EED Energy Efficiency Directive
EEO Energy Efficiency Obligations EEPs Energy Efficiency Programs
EERS Energy Efficiency Resource Standards EIA Energy Information Administration
EPBD Energy Performance of Buildings Directive
EU European Union
ewz Elektrizitätswerk der Stadt Zürich FCM Forward Capacity Market
GAM Generalized Additive Model
HVAC Heating, Ventilation, Air Conditioning IAQ Indoor Air Quality
IDC Indice de Dépense de Chaleur
LASSO Least Absolute Shrinkage and Selection Operator LBNL Lawrence Berkeley National Laboratory
LCSE levelized cost of saved energy
LEAN Low-Income Energy Affordability Network LIHEAP Low-income Home Energy Assistance Program MEEA Midwest Energy Efficiency Alliance
MoPEC Modèle de prescriptions énergétiques des cantons MVHR Mechanical Ventilation with Heat Recovery
NEEA Northwest Energy Efficiency Alliance NEEP Northeast Energy Efficiency Partnerships OLS Ordinary Least Squares
Pas Program Administrators PBF Public Benefit Funds
PIBRVs Pressure Independent Balancing Radiator Valves
PMV-PPD Predicted Mean Vote-Predicted Percentage of Dissatisfied REED Regional Energy Efficiency Database
RGGI Regional Greenhouse Gas Initiatives SFOE Swiss Federal Office for Energy
SIA Swiss Society of Engineers and Architects SIG Services Industriels de Genève
SRE Surface de Référence Énergétique des bâtiments (total heated floor area), TFC Total Final energy Consumption
TRVs Thermostatic Radiator Valves
US United States
VIF Variance-Inflation Factors
WAP Weatherization Assistance Program
Introduction
The Paris Agreement reinforced and has helped to accelerate sustainable energy transition that can transform the energy system from fossil fuel-based to zero carbon while creating economic values. Committed to decarbonization, countries have focused on renewable energy and energy efficiency measures that can potentially achieve 90% of the required carbon reductions [1]. Enhancement of energy efficiency is essential to make faster progress towards the sustainable energy transition.
With growing focus on energy efficiency as one of the key elements of the sustainable energy transition [2], [3], comprehensive actions have been developed in coordination of multi-dimensional perspectives associated with technology, infrastructure, policy and society [4], [5]. Given high uncertainty about future energy market and policies, long-lasting and measurable impact does not rely solely on long-term improvement of energy efficient
technologies but also on concrete policies and actions today that foster investment in energy efficiency under present-day conditions and motivate adoption of the technologies. These are quintessential because they allow to build up the required human capacity and to identify, test and further implement energy efficiency technologies and the associated policy instruments.
Energy policies are developed at regional, state and federal/cantonal level under which respective governments set targets and adopt policy instruments with energy efficient
measures. Since the evaluation of the policy actions is often weak or entirely missing, impact of the adopted instruments and measures should be thoroughly assessed and the results need to be disseminated to key actors across all dimensions to generate collective actions based on a shared understanding.
Therefore, this thesis focuses on the one hand on a policy instrument that delivers electric energy savings and on the other hand on technical energy efficiency measures implemented in buildings in the context of Swiss energy transition. By sharing information on the results of multiple interventions with diverse actors, we contribute to leveraging knowledge-based interventions for enhancing energy efficiency.
Swiss climate and energy strategy
Switzerland plans to reach zero net carbon emissions by 2050 to limit global warming to 1.5°C [6]. The Swiss government signed the Paris Agreement and set short and long-term targets for greenhouse gas (GHG) reduction based on the finding of Intergovernmental Panel on Climate Change (IPCC) [7]. Under the Paris Agreement, Switzerland committed itself to reduce its GHG emissions by 20% by 2020 and by 50% by 2030 against base year level of 1990 [8]. Federal government set the long-term goal of 70 to 85% of reduction by 2050 with at least 30% achieved domestically and the rest carried out abroad to maintain the global temperature increase below 1.5°C [9]. This corresponds to up to 95% of emission reduction by 2050 from industry, transport and buildings [6].
Guided by the ambitious long-term CO2 emission reduction goal, Switzerland developed a legal and policy package, the Energy Strategy 2050. Aiming to advance Swiss energy transition towards low-carbon economy, the strategy is structured around three main pillars, 1) phase-out of nuclear energy, 2) reduction of energy consumption, and 3) promotion of renewable energy and energy efficiency. The Swiss Energy Strategy’s targets represent a per capita reduction of final energy consumption by 16% by 2020, 43% by 2035, and by 54%
by 2050 against the 2000 base year level. Per capita electricity consumption is to be reduced by 3% by 2020, 13% by 2035, and 18% by 2050 compared with the base level of year of 2000 [10], [11]. Accordingly, laws, ordinances and policy measures have been revised and newly developed.
Energy use and CO
2emissions by sector in Switzerland
Total final energy consumption (TFC) in Switzerland in 2018 was 19.8Mtoe [12]. The second- largest energy consumer in Switzerland (after the transport sector) is the residential sector with a 29% share of the TFC, followed by the industry sector with 19% in 2018 [13]. Heating in buildings accounts for the largest share of energy consumption in the residential sector [10]. In 2018, the energy-related CO2 emissions in Switzerland reached 36 million tonnes, showing a reduction of 12% compared to year of 1990 and 18% since 2005 [13]. The transport sector is the largest contributor to energy-related CO2 emissions with 43% of total share due to combustion of diesel and gasoline. The second largest emitter is the residential sector with 22%. Oil is the main source of CO2 emissions accounting for 70% of the total
41% of final energy use and 25% of energy-related CO2 emissions are attributable to
buildings in Switzerland [14]. Thus, buildings have been a main target of energy and climate policy. It is projected that Switzerland has to reduce CO2 emissions from buildings by at least 50% from the 1990 level by 2026 and 2027 to meet the goals [15]. The final energy demand from building stocks should be reduced by 60% between 2020 and 2050 [16].
Considering share of TFC by source, electricity is a key to achieving the goals. Switzerland has already surpassed its 2020 target of 3% reduction of per capita electricity consumption in 2017 reaching 5% of reduction compared with 2000 level [17]. Electricity consumption in Switzerland has already decoupled from population growth since 2005 [18]. However, more electricity demand is clearly foreseen due to more installations of heat pumps and more electric mobility [19]. Thus, reducing the electricity demand through energy efficiency gains has been increasingly focused as a cleanest and most cost-effective solution [20].
Policy instruments and measures
Multiple policy instruments have been adopted at federal and cantonal level to reduce emissions and improve energy efficiency.
1.3.1 CO
2levy
The CO2 levy is imposed on stationary fuels such as heating oil and natural gas to encourage usage of low carbon sources. For the years 2008-2009, the CO2 levy was 12 CHF/tCO2, which increased to 36 CHF/tCO2 for from 2010 to 2013. In 2014, the CO2 levy on thermal fuel was increased to 60 CHF/tCO2 [21] and further raised to 96 CHF/tCO2 in 2018. The rate is increased if the greenhouse gas emission reduction targets are not met. The existing CO2
law sets maximum tax rate at 120 CHF [22]. Following the results of referendum in 2021, the maximum tax rate could be increased to 210 CHF [23], [24].
1.3.2 CO
2levy reimbursement and grid surcharge
Large carbon-intensive companies are exempted from the CO2 levy if they commit
themselves to reduce their GHG emissions or participate in the emissions trading scheme
[25]. Industry and service sector companies that are engaged in the activities listed in the CO2 Ordinance, Annex 7 [26] and emitting at least 100 tonnes CO2 (equivalent to
approximately 38000 litres of heating oil or 500000 kWh of natural gas) are exempted from the CO2 levy if they sign a target agreement or a cantonal scheme [27]. The grid surcharge amounting to a maximum 1.5 cents per kWh is waived for energy-intensive industries whose electricity costs amount to at least 10% of their gross value if they signed a target agreement on energy efficiency and invested 20% of the saved grid surcharge into energy efficiency measures [28], [29].
1.3.3 EE tenders for more efficient electricity use
Using the revenue, the Swiss Federal Office funds projects and programs for more efficient use of electricity in industry, the service sector and households on the basis of a competitive tender process, which is called ‘ProKilowatt’ [30]. The main aim is to fund appliances/
process measures with payback period of longer than five years and infrastructure, projects with a payback period of more than nine years [31]. The overall budget increased from 6 million CHF in 2010 to 45 million CHF in 2016. Between 2010 and 2019, 241 million CHF was spent and annual electricity saving of 743 GWh was achieved.
1.3.4 Building program
One third of revenue generated with the CO2 levy is used to finance energy efficient renovations in buildings. Since 2018, a maximum of 450 million CHF of CO2 levy has been used annually for building program and for the promotion of heat pumps [14]. The program has two components, one at federal and the other at cantonal level [28].
1.3.5 Electric appliances
To be aligned with EU legislation, Switzerland partly adopted the EU Energy Efficiency Directive (EnV 730.01, 1998R) that prescribe minimum energy performance and energy labelling [9]. Switzerland implements more stringent requirements for certain household appliances than the EU [32]
1.3.6 Policies across different levels of government
Federal policies
The policy measures explained above are all implemented at the federal level. This is also the case for some policies. Further policies were implemented by the federal government in order to support renewable energy and waste heat recovery and to set maximum CO2
emissions for vehicles and minimum energy performance standards for appliances [14]. The Swiss Society of Engineers and Architects (SIA), as a group of specialists for construction, technology and environment in Switzerland, develops standards and regulatory guidelines. A voluntary label for new and refurbished energy efficient buildings called Minergie was
launched in 1998. Minergie has widened its scope starting from passive housing in 2001 (Minergie P), targeting health, comfort and building ecology (Minergie-Eco) to nearly zero energy building addressing European Union (EU) level guidelines (Minergie A) [33].
Cantonal policies
According to Art. 89 para. 4 of the Swiss Federal Constitution, the cantons are responsible for measures concerning energy consumption in buildings, while federal government plays only a subsidiary role [34]. The cantons are required to set building regulations (Modèle de prescriptions énergétiques des cantons (MoPEC)) with defining minimum energy
performance standards in buildings. MoPEC requirements were tightened significantly, with MoPEC 2014 regulations being comparable to the (regular) Minergie levels. However, the Minergie P that focuses on the reduction of heat demand requires 30% less thermal energy than MoPEC [34], [35]. Cantons harmonized their building codes across the country while maintaining a certain level of flexibility to address cantonal situation and set standards for zero energy buildings being aligned with EU recommendations [10]. The cantons introduced cantonal energy certificate to report the overall energy efficiency of buildings (Certificat Énergétique Cantonal des Bâtiments, CECB). The CECB is applied to residential buildings, important types of service sector buildings and schools [36]. The CECB experts carry out an energy audit and check compliance with MoPEC for heating, domestic hot water, ventilation and air conditioning, and SIA380/1 (2016 edition) for standard values for heat demand and SIA 387/4 (2017 edition) for relative energy demand for lighting [37]. The auditors provide owners of buildings with an audit report with technical recommendations on how to improve the building’s energy efficiency.
1.3.7 Discussion of policy measures
The CO2 levy has proven its effectiveness in reducing emissions by encouraging low carbon energy sources [38]. The competitive tender process has also been identified as an effective policy instrument [39]. However, the tendering process and the resulting competition across the submitting parties implies that particular low-risk and high return measures are supported [40].
Although Switzerland has not implemented an Energy Efficiency Obligation scheme (EEO) a number of energy utilities have been voluntarily implementing Energy Efficiency Programs (EEPs) to reduce electricity consumption. Boogen et al. estimated the average cost- effectiveness for 30 Swiss utilities at 0.040 CHF/kWh, while the average price of saving electricity through the tendering is 0.027 CHF/kWh [41], [42].
Scope of research
In Switzerland, a mix of policies including economy-wide, sector-specific and technology- specific measures have been implemented, with the objective of ensuring the effective use of public funds. Generally speaking, policy measures must be continually adapted as progress is made towards the goals, as new technologies and techniques become available and experience is made with the existing ones. It is hence relevant both at the policy design level and at the implementation level to ensure that the effective and cost-effective mix of
measures is implemented.
Figure 1-1 Projected energy savings across key measures due to Energy Strategy 2050 (*2050 NEP represents results of new energy policy (NEP) scenario) [43]
As illustrated in Figure 1-1, air conditioning and ventilation are projected to consume more energy (+71%). This is both due to controlled ventilation systems in high-performance buildings and increased use of air-conditioning in summer due to higher future average outdoor temperatures as a consequence of climate change [44]. While mechanical ventilation with heat recovery (MVHR) causes an increase in final energy use due to continuous
operation it offers even larger heat savings associated to the supply of space heating (64%) and/or domestic hot water. Overall, the largest drop in final energy consumption is expected to be achieved in space heating followed by domestic mobility. This raises questions about the operational factors that cause energy losses in heating and ventilation systems and whether the full suite of opportunities is known and already exploited to minimize energy use in these areas. In this thesis, two energy efficiency measures which do not receive much attention nowadays will be investigated in detail through field observation and scenario simulations to provide measure specific solutions.
Canton of Geneva makes use of a wide range of energy efficiency measures including heating, air conditioning and ventilation. [37]. For example, according to cantonal regulations in Geneva hydraulic balancing, i.e. a process to optimize the heat distribution, is mandatory [37], raising for example the question whether other cantons should follow this example. To
-64%
-16%
-43%
+71%
-18%
-54%
+29%
0 100 200 300 400 500 600 700 800 900
2010 2050 NEP
Other
Domestic mobility Motors and drives Electronics Air conditioning, ventilation etc.
Lighting Process heat Hot water Space heat
Final energy use in PJ
-57%
-46%
allow to identify the energy efficiency gap and to suggest operational practices that maximize energy savings, the following research questions are addressed.
1. What is the energy savings potential from hydraulic balancing in buildings in Geneva?
2. What type of buildings should be targeted to maximize energy savings by hydraulic balancing?
Ventilation systems have been primarily implemented to ensure good air quality but they also play an important role for saving energy. To meet the required energy performance level in new buildings, heat recovery system that recovers heat from exhaust air is inevitable, i.e.
mechanical ventilation with heat recovery (MVHR) is commonly applied [45]. However, MVHR requires proper air-tightness that old buildings generally do not have, and it consumes significant amount of energy due to its continuous operation throughout the year. Against this background, hybrid ventilation that switches between natural and mechanical modes and has wider applicability has been emerging as an alternative solution for energy efficient
ventilation. A few buildings in Geneva recently installed the hybrid ventilation system, raising the question how well they perform. We therefore aim to answer following questions.
1. How much energy can be saved per year by hybrid ventilation compared to the other commonly used energy efficient ventilation systems such as mechanical ventilation with heat recovery (MVHR)?
2. Does hybrid ventilation ensure good indoor air quality meeting the Swiss standards?
As mentioned above, a number of Swiss utilities have been voluntarily running EEPs.
According to the national benchmarking study of EEPs in Switzerland in the year of 2017/18, 53 out of a total of 650 energy utilities implemented EEPs [46-47] and these 53 utilities roughly represent 50% of all end users in Switzerland. Nearly all of them were put into place no longer than 15 years ago. Compared to the United States where EEPs have been
operated for the past 45 years the experience made in Switzerland (and in Europe) is relatively limited. The early experience in Switzerland and the potential need to motivate other utilities to operate EEPs raises questions about the evolution, exemplary approaches and best proven practices from the US. The research questions are as follows.
1. What types of policy instruments are effective in encouraging utilities to implement EEPs?
2. What are the best proven practices that ensure cost-effective implementation of
The chapters II-V are each based on one scientific article addressing following research questions (Table 1-1).
Table 1-1 Structure of the thesis
Research question Scope Methodology Chapter number and title - What is the energy savings
potential from hydraulic balancing in buildings in Geneva?
Cantonal (Geneva)
Scenario analysis
Chapter 2. Estimation of energy savings potential through hydraulic
balancing of heating systems in buildings
- What type of buildings should be targeted to maximize energy saving from hydraulic
balancing?
Cantonal (Geneva)
Case study evaluations Statistical
analysis
Chapter 3. Identification of criteria for the selection of buildings with elevated energy saving potentials
from hydraulic balancing - Methodology and case study - How much energy can be saved
per year by hybrid ventilation compared to the other commonly used energy efficient ventilation systems such as mechanical ventilation with heat recovery (MVHR)?
- Does hybrid ventilation ensure good indoor air quality meeting the Swiss standards?
Cantonal (Geneva)
Case study evaluations Statistical
analysis Interviews
Chapter 4. Evaluation of performance of energy efficient
hybrid ventilation system and analysis of occupant behavior to
control windows
- What types of policy instruments are effective in encouraging utilities to implement energy efficiency programs (EEPs)?
- What are the best operational practices that ensure cost- effective implementation of EEPs?
National (Switzerland
and the United States)
Cross-country comparative
study
Chapter 5. Comparative analysis of customer-funded energy efficiency programs in the United States and
Switzerland
Estimation of energy savings potential through hydronic balancing - A case study in Geneva, Switzerland
1Haein Cho, Daniel Cabrera, Martin K. Patel
Chair in Energy Efficiency, Institute for Environmental Sciences (ISE) and Department F.-A. Forel for Environmental and Aquatic Sciences (DEFSE), Faculty of Science, University of Geneva, 1211 Geneva 4, Switzerland
Abstract
Hydraulic balancing of heat distribution systems in buildings plays an important role in improving indoor thermal conditions as well as reducing energy consumption for space heating. Building energy models typically do not incorporate thermal comfort while it is essential for hydraulic balancing to do so. This paper proposes a methodology that assesses the impact of hydraulic balancing on the energy savings potential while ensuring thermal comfort of the occupants. The proposed methodology is composed of three parts, i.e. characterization, estimation and scenario analysis. First, we characterize indoor air temperature level of each individual flat in buildings belonging to the same central heating system to examine temperature variation among the flats.
Second, thermal comfort under given indoor conditions is predicted by means of the PMV-PPD model (predicted mean vote-predicted percentage of dissatisfied) and the optimal indoor air temperature where most of the occupants feel comfortable is estimated. We calculate potential energy savings assuming that hydraulic balancing ensures ideal conditions where the indoor air temperature stays at the optimal level, thereby maintaining thermal acceptance of at least 94% of the occupants. A scenario analysis covering different types of occupant behavior allows to estimate the energy savings potential under different conditions. We test the applicability of the proposed methodology by applying it to a group of existing buildings in Geneva, Switzerland during the cold season. The case study demonstrates that there is an energy savings potential between 2% to 14%. We also discuss how the simulation results can guide practitioners operating hydraulic balancing programs.
Keywords: thermal energy savings; thermal comfort; PMV-PPD; hydraulic balancing
Introduction
Heating and cooling accounts for 50% of the final energy demand in the European Union;
heating alone represents nearly 65% of the final energy in the EU’s residential sector [48]. In Switzerland, buildings represent almost 40% of the total final energy demand of which three quarters are used for space heating/cooling and water heating [49]. Switzerland has
implemented mandatory building energy standards to limit energy use for heating in buildings. While new buildings have to comply with increasingly strict building energy standards, the main challenge is to improve the energy efficiency of the existing building stock [50]. Existing buildings offer large potentials ([51], [52]) for saving thermal energy which can be leveraged 1) by focusing on thermal insulation of the building envelope [5], 2) by implementing a new, more efficient heating system or 3) by optimizing the operation of the existing heating system [5], [14]–[19].
The first two options can be implemented in different ways and can significantly reduce heat losses. Nowadays the degree of thermal retrofitting differs widely, ranging from single measures delivering small overall improvements to deep comprehensive retrofits that may enhance the buildings’ energy efficiency performance by 50% or more [53], [54]. However, the latter implies relatively high investment costs and are characterized by long payback periods [5], [20]. Compared to new buildings that allow to ensure a high level of energy efficiency at relatively low additional cost, the cost and effort for retrofitting existing buildings is substantial [55]. Frequently, building owners lack the financial means to cover the large up- front investment, regardless whether they have access to subsidies or not [56]. Compared to thermal insulation of the building envelope and installation of a new heating system, the third option of optimizing the existing heating system represents a cost-effective and relatively simple way to save energy, albeit typically resulting in more limited energy savings [57].
Buildings which have not undergone hydraulic balancing are typically subject to uneven temperature distribution and wastage of energy [58]. By fine-tuning the hydraulic (e.g. flow rates, pressure drops, etc.) and thermal (e.g. fluid temperatures, room air temperatures, etc.) operational conditions, hydraulic balancing seeks an improved thermal and hydraulic
equilibrium [59]. Considering that some hydraulic systems contain thousands of pipes, balancing valves and radiators, and that each of them influences flow rates and heat transfer rates, computer simulation is widely applied to describe the thermal and hydraulic operational conditions [60], [61]. For example, Lefter and Popescu analyzed different scenarios of
operation by adjusting the settings of balancing valves and by application of differential pressure controllers [62]. Cholewa et al. estimated that the implementation of diverse valves
such as TRVs (Thermostatic Radiator Valves), DPCVs (Differential Pressure Control Valves) and PIBRVs (Pressure Independent Balancing Radiator Valves) under different hydraulic operational conditions allow to save 14.6% and 23.8% of energy [63]. The literature review indicates that more attention is paid to the benefits of energy savings than to the aspects of thermal comfort, in spite of the importance of the latter and its interrelation with thermal building performance [64] [65].
Most building energy models taking thermal comfort into account consider only indoor temperature. Typically, these models apply a static range of indoor air temperatures as condition for thermal comfort. They usually do not consider parameters such as thermo- physical properties of buildings [66], building operational practices, outdoor conditions and occupant behavior [67]. Consequently, models simulating the indoor conditions without factoring in these variables do not represent reality well [68], and simulation-based operation can lead to thermal discomfort of the occupants in combination with high energy
consumption [67], [69].
This raises questions of how to best describe the relationship between indoor and outdoor conditions with thermal comfort as well as with energy use. To this end, the present study proposes a methodology that uses data collected from monitoring of existing buildings.
Focusing on particularly important factors representing the relationship between occupants and building systems and outdoor condition, we assess thermal comfort and we quantify energy savings potentials. The proposed methodology allows to obtain various practical insights, e.g. into trade-offs between energy savings potential and occupants’ thermal comfort.
The next section explains the methodology (section 2.2) followed by a case study where we apply the proposed methodology to existing buildings in Geneva, Switzerland (section 2.3).
We discuss the results (section 2.4) and draw conclusions in section 2.5, thereby providing suggestions on how to make hydraulic balancing more effective.
Methodology
The methodology is largely composed of three parts, i.e. 1) characterization of thermal conditions and 2) estimation of energy savings and 3) scenario analysis (Figure 2-1). We first characterize indoor air temperature and relative humidity in the building over the study period to understand temperature fluctuations and its variation among flats. Then, we investigate
of occupants under the given building conditions is then estimated. At this step, we aim to understand 1) whether occupants feel thermally neutral or uncomfortable, 2) what causes thermal discomfort - whether it is from feeling cold or hot, and 3) if the causes of discomfort differ among flats (e.g. some flats feeling cold discomfort while others feeling hot discomfort).
The step of characterization allows us to determine whether the hydraulic balancing is necessary or not. Applying both measured data and standard values to the equations, we develop a statistical multivariate model of thermally comfortable indoor air temperature as a function of outdoor temperature. We then estimate the optimal indoor air temperature where all occupants feel thermally comfortable. Comparing the optimal state that can be achieved through hydraulic balancing to the current state, we estimate the energy savings potential.
We build a number of scenarios to represent a variety of outdoor conditions and occupant behaviors. For each scenario, we apply the method just described.
Figure 2-1 Concept of developed methodology; steps and purpose of each step
2.2.1 Data collection
As study period, we choose the cold season between December 2016 and March 2017.
Seven different types of input data are required to apply the methodology outlines above. To characterize indoor air conditions over time and to find any influence of outdoor air
temperature over deviation of indoor air temperature and relative humidity, we require hourly data on indoor air temperature, relative humidity and outdoor air temperature (three
parameters). Evaluation of thermal comfort is based on the so-called predicted mean vote- predicted percentage of dissatisfied (PMV-PPD) model [70] which requires six different input data, namely: indoor air temperature, relative humidity, mean radiant temperature, metabolic rate), clothing insulation, and indoor air velocity which are described in Table 2-1. The estimated thermal comfort level is compared with the range required according to European and Swiss standards. The energy savings potential is determined based on the change in heat demand which is calculated using indoor and outdoor air temperature data (see below, section 5) Energy savings potential for space heating). In order to test the methodology, we gather data for the seven variables listed in Table 2-1. As explained in the table, the first three parameters are measured and mean radiant temperature is assumed to be the same as indoor air temperature, which is in line with earlier work [71] [72]–[74]. Outdoor air temperature was collected from a local weather station [75]. To measure indoor air temperature and relative humidity, indoor sensors that simultaneously measure both parameters were installed. Each flat was equipped with such a multi-use sensor which was installed at the height of 1.5m either in the living room or the hallway, protecting the sensors from external influences such as sudden air infiltration due to window movement or
convective process occurring in the bathroom. For the three remaining factors, we extract values that are determined in compliance with the SIA 2024 standard [76] and we apply them as fixed values. For near-sedentary physical activities, the metabolic rate ranges between 1.0MET (e.g., lying quietly) and 1.3MET (reading-sitting) [77], [78]. Following the standard SIA 2024, we chose a single value of 1.2MET. The level of clothing insulation is assumed to be 0.8clo and 1.0clo representing thin and thick clothes during winter respectively. Because SIA 2024 indicates only maximum air velocity, we use an average air velocity of 0.1m/s for closed windows (according to Nicol, Humphreys and Roaf’s book [79]). During winter, people tend to spend more time indoors with the closed windows [80], so we applied a fixed value of 0.1m/s as an air velocity.
Table 2-1 Description of factors applied to thermal comfort estimation
Factor (unit) Definition (physical unit) Value used in the
study References
Outdoor
temperature (°C) Hourly outdoor temperature Measured University of Geneva Indoor air
temperature (°C) Indoor temperature Measured (one sensor for each flat)
Measured by real time hourly monitoring system
of local company Relative humidity
(%)
Percentage of water vapor present in the air (%)
Measured (one sensor for each flat) Mean radiant
temperature (°C)
Mean radiation heat transfer between body and surrounding surfaces
Assumed as same as
indoor temperature [71] [72]–[74]
Metabolic rate (MET)
- level of internal heat generation by various activities - 1 MET= 58W/m2
1.2 [76]
Clothing insulation (clo)
-Level of heat gains and losses by clothing
-1 clo=0.155m2∙K/W
0.8 (winter light clothes)
[76]
1.0 (winter thick clothes)
Air velocity (m/s) Rate of air movement 0.1 [79]
2.2.2 Building characteristics
Our case study consists of a group of four multi-family buildings located in Geneva,
Switzerland. The buildings were constructed between 1961 and 1970, they have six storeys and contain 96 flats (15 flats each in building 1 and 2, 36 flats in building 3 and 30 flats in building 4). As Figure 2-2 illustrates, the buildings have different sizes and orientations. In terms of total heated floor area, building 1 and building 2 are comparable in size (1342m2 and 1368m2, respectively), while building 3 and 4 are larger (2045 m2 and 3237 m2). The four buildings share one central natural gas-fired boiler, which is located in building 3 (Figure 2-2).
The boiler was modernized in 1992 and its capacity is 500kW. The average return temperature from the radiators was around 32°C during the studied period. Supply temperatures range between 35°C and 45°C. They are controlled by a heating curve2 representing a linear relationship between outdoor temperature and supply temperature. All
2 The heating curve provides, for example, the following setpoints: supply temperature 47°C at outdoor temperature of 5°C and supply temperature 37°C at outdoor temperature of 15°C.
flats are heated with radiators, which are equipped with thermostatic radiator valves (TRVs).
The annual final energy consumption for space heating and hot water between May 2016 and April 2017 amounted to 388 MJ/m2/year.
Figure 2-2 Map of buildings that are examined as a case study
2.2.3 Processing of temperature data
Data cleaning
To avoid possible data errors, we undertook a data cleansing process. Extreme outliers that lie abnormal distance from other values are excluded and only the values between the 5th and 95th percentile are used. We observed that some data loggers did not work properly and hence reported the same temperature for the entire period for some flats. We removed flats with identical minimum and maximum temperature, or standard deviation of zero. Flats with large number of missing values (for more than 70% of observations) were also removed.
Identification of groups
Monitored indoor temperature data is classified into several groups according to different ranges of outdoor air temperature. Unlike statistical tests that distinguish between dependent and independent variables, this allows us to carry out explanatory data analysis that divides
Although indoor air temperature is more strongly correlated with outdoor air temperature under cold conditions compared to moderate conditions [82], it is essential to study indoor thermal conditions in response to change in outdoor air temperature [83]. Hence, we calculate daily average outdoor air temperature using hourly data and we classify them into four different ranges that cover the lowest and highest outdoor temperature between December 2016 and March 2017, (category I: between -6 and 0°C, category II: between 0 and 5°C, category III: between 5 and 10°C, category IV: between 10 and 15°C). As heating occurs when daily mean outdoor air temperature is below 15°C [83], [84], we only considered days with outdoor air temperature below this level.
Regression Analysis
The regression analysis was carried out to relate monitored indoor air temperature with outdoor air temperature for each flat. The analysis allows us to estimate the impact of a unit difference in outdoor air temperature on indoor air temperature. Moreover, by computing mean and standard deviation of monitored indoor air temperature across flats, we show the average indoor air temperature profile as well as its standard deviation across a large pool of individual flats.
2.2.4 Thermal Sensation Model
Proposed by Fanger [70], the PMV-PPD model has been internationally accepted as an indicator to determine thermal comfort level. The PMV model was developed for application to artificially controlled environments subject to heating, ventilation and air-conditioning.
Previous studies found that PMV-PPD model reflects well the population’s real comfort level [85], [86]. The predicted mean vote (PMV) value provides the mean value of the votes representing the same environmental condition on a seven-tiered scale of thermal sensation (-3: hot, -2: warm, -1: slightly warm, 0: neutral, 1: slightly cool, 2: cool, 3: cold). The predicted percentage of dissatisfied (PPD) value represents the percentage of people who are
dissatisfied with the given thermal conditions. The PMV and PPD values are computed using Fanger’s equations [70], [87]: As the PMV value deviates from zero (zero stands for
thermally neutral), the PPD value increases in a non-linear fashion [70]. The PPD ranges between 5% and 80%, which means that there will always be at least 5% of population who are not satisfied with the given thermal condition.
By considering the metabolic rate and clothing, the PMV-PPD model accounts not only for environmental factors (relative humidity, indoor air temperature, mean radiant temperature, air velocity) but also human behavioral factors (metabolic rate, clothing insulation) [88].
According to European Standard EN16789 (which is a modified version of the EN15251 that addresses energy performance of buildings and sets standards for indoor environmental conditions), four PMV-PPD classes are established and recommended ranges are provided alongside. In the Table 2-5 in the Appendix, these are given together with a short description of each class.
2.2.5 Energy savings potential for space heating
Optimal indoor air temperature
We carry out regression analysis to relate indoor air temperature with PMV values. This allows us to subsequently estimate a comfortable temperature range in which the PMV value ranges between -0.2 and 0.2, i.e. where the PPD rate is less than 6% [89]. We calculate the mean of the estimated comfortable temperature range of each flat in order to find the optimal indoor air temperature that is commonly accepted by most of the occupants in the four buildings.
Heating demand reduction
By balancing the hydraulic system and regulating the temperature within the hydraulic system of a building, heat losses in the heating distribution network are decreased and consequently, energy wastage is also reduced. Assuming that all flats stay at the estimated optimal indoor air temperature and physical building characteristics do not change over time, we calculate the annual space heating demand using the Equation 2-1 which also serves to evaluate the potential energy demand reduction.
Equation 2-1
𝑄𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔= 0.001 ∗ ∆𝑇 ∗ 𝑡ℎ𝑒𝑎𝑡𝑖𝑛𝑔∗ [𝑆 ∗ 𝑈 + 𝑉 ∗ 𝑞 ∗ (1 − 𝑟𝑒𝑐) ∗ 𝐶𝑎𝑖𝑟] Where
Qbuilding is the building’s useful annual space heating demand [kWh/a],
∆T is the difference between indoor and outdoor air temperature [K]
theating is the length of the annual heating season in terms of hours [h/a],
S is surface area of the building envelope, consisting of exterior walls, windows, floors, roof [m2],
U is the average thermal transmission coefficient derived of the building envelope, established based on envelope area-weighted specific U-values [W/m2/K],
V is heated building volume [m3],
q is hourly air exchange per unit of heated building volume [m3/h/m3], rec is the fraction of heat recovered from outgoing air [-],
Cair is specific heat capacity of air [0.343 Wh/m3/K],
2.2.6 Application of methodology
To validate the methodology developed in this study, we selected 4 buildings belonging to the same central heating system. 76 flats were randomly selected in a way to represent each floor and each building. We then monitored and collected indoor and outdoor air temperature and relative humidity data during cold season between December 2016 and March 2017, and applied the proposed methodology.
Results
2.3.1 Indoor temperature
Average indoor air temperature
Figure 2-3 illustrates the measured average daily indoor air temperature for the selected 76 flats (in grey) and the average temperature for all flats (in red). The latter is maintained between 21 and 25°C satisfying class I criteria according to EU norm (EN15251 [90]).
However, when evaluated against the Swiss standard reference value, which is supposed to lie between 20 and 24°C (SIA 382/1 [91]), 61 flats are found to be overheated for at least three days during the study period and five flats are overheated on more than 50 days.
Figure 2-4 shows average hourly indoor air temperature profile during one month (January 2017). Apart from few cases, the temperature profiles are nearly completely flat and the average value barely fluctuates throughout the 24 hours of a winter day. As previous studies showed, temperature is varied from day to night during summer [1], but during the cold season, indoor air temperature rarely varies during daytime. Furthermore, in the absence of night setback control during the considered month, the supply temperature of the heating system in the buildings is determined only based on outdoor temperature without being switched from day to night. [92]. While indoor air temperature is well controlled throughout the full day (24 hours), temperature dispersion among flats is quite high reaching around 5°C.
Figure 2-3 Measured average daily indoor air temperature changes during winter
Figure 2-4 Measured average hourly indoor air temperature throughout a day in winter
Average indoor relative humidity
The average daily indoor relative humidity for all 76 flats and the average relative humidity for all flats are illustrated in Figure 2-5 in grey and red, respectively. The average relative
humidity fluctuated between 20% and 40%, reaching its lowest values in December 2016 and January 2017. Compared to the EU norm (EN15251 [90]) and Swiss standards (SIA 382/1 [91]) that both define a range between 30% and 50% as desirable, 75 flats show at least five dry days and 33 flats underwent at least 33 dry days reaching below 30%
throughout the study period. We attribute the drier indoor conditions to the relatively high indoor air temperature during the same period (Figure 2-3). This is potentially explained by low outdoor temperature. The colder outdoor air becomes, the less water vapor it holds. As cold air containing a small amount of water vapor enters indoor and is heated, relative humidity drops.
Figure 2-6 shows average hourly relative humidity profile during one month (January 2017).
Flats are showing somewhat varied profiles, and the entire building demonstrates high dispersion of relative humidity among flats. As the comparison of Figure 2-4 and Figure 2-6 shows, relative humidity varies more than indoor air temperature, which is confirmed by earlier studies (e.g. [93]).
Figure 2-5 Measured average daily indoor relative humidity changes during winter
Figure 2-6 Measured average hourly indoor relative humidity throughout a day in winter
2.3.2 Dispersion of indoor air temperature and relative humidity
Temperature variation among flats
As one of the main objectives of this study, we investigate the variability in indoor air temperature among 76 flats of all four buildings over time. We determine the maximum difference in daily average temperature among all 76 flats for every day because thermal discomfort is associated with extreme heat or cold. As Figure 2-7 shows, average
temperature differences of almost 10°C occur and differences of 4-6°C are quite common.
The comparison of indoor and outdoor air temperatures indicates higher variability when outdoor air temperature is below 0°C.
Standard deviation of indoor temperature that measures the average distance of the data from the mean was analyzed across the four buildings over four months (between December 2016 and March 2017; Figure 2-8). This confirms that cold outdoor conditions leads to high dispersion of indoor temperature. Comparing January 2017, which was the coldest month (- 0.67°C on average) with March 2017 (warmest month, 9.13°C on average), we found a decrease of the standard deviation of indoor temperature by 44%.
Figure 2-7 Variation of the indoor air temperature among all 76 flats as a function of the outdoor air temperature during winter
Figure 2-8 Standard deviation of indoor temperature of individual building and mean monthly outdoor temperature across four months of study period
Analysis of group differences
For four ranges of outdoor air temperature, Table 2-2 and Figure 2-9 shows the indoor air temperature spread across the flats. The colder it is outside, the wider the range of indoor temperatures is. The standard deviations for the outdoor air temperature categories I and II exceeds those of the categories III and IV by almost 0.3°C.
A study conducted in the UK showed the same type of pattern which was however more pronounced, with the standard deviations at standardized outdoor air temperature of 0°C exceeding that of 10°C by almost 1°C [94]. Mean indoor air temperature slightly increases as outdoor air temperature goes down. Previous studies have reported a stronger correlation between outdoor and indoor temperature when outdoor air temperature is above 15°C than at cooler outdoor air temperatures [82]. Other studies indicate that the association between indoor and outdoor air temperature is non-linear, particularly during heating season.
Table 2-2 Description of average daily temperature distribution across different outdoor air temperature categories (all values in °C)
I (-6°C – 0°C) II (0°C – 5°C) III (5°C – 10°C) IV (10°C – 15°C)
Mean (°C) 23.1 22.9 22.2 22.0
Standard
Deviation (°C) 1.3 1.3 1.1 1.0
Median (°C) 23.2 22.8 22.1 22.0
We proceed by analogy for relative humidity. As shown in the table below Figure 2-10, standard deviation of relative humidity becomes smaller as the outdoor air temperature increases. However, the mean humidity increases from 25 to 38%, as the outdoor air
temperature ascends (see table below Figure 2-10). As the indoor air temperature increases, the amount of water molecules that air can hold increases resulting in lower value of relative humidity.
I (-6°C - 0°C) II (0°C - 5°C) III (6°C - 10°C) IV (10°C - 15°C)
Mean 23.05 22.87 22.18 22.01
Standard
deviation 1.32 1.31 1.08 1.02
Median 23.20 22.80 22.10 22.00
Figure 2-9 Dispersion of hourly indoor air temperature of 76 flats across different outdoor air temperature categories (the median is the center horizontal line inside the rectangle, the ends of the
rectangle are 25 and 75 percentiles, and the whiskers show the 5 and 95 percentiles)
I (-6°C - 0°C) II (0°C - 5°C) III (6°C - 10°C) IV (10°C - 15°C)
Mean 25.10 31.80 36.85 38.32
Standard
deviation 6.57 6.99 6.58 6.38
Median 24.00 31.00 36.00 38.00
Figure 2-10 Dispersion of hourly relative humidity of 76 flats across different outdoor air temperature categories (the median is the center horizontal line inside the rectangle, the ends of the rectangle are
25 and 75 percentiles, and the whiskers show the 5 and 95 percentiles)
Regression
We employ a non-linear model to represent the effect of unit change of outdoor air
temperature on indoor air temperature. Mean hourly indoor air temperature is regressed on outdoor air temperature along with squared term to allow for non-linear relationships using Ordinary Least Squares (OLS). As shown in Figure 2-11, the curves exhibit an increase of indoor temperature as outdoor air temperature decreases (in line with the pattern displayed in Figure 2-9). Additionally, we plot standard deviation across outdoor air temperature, which appears to be exponentially increasing especially when outdoor air temperature drops below 0°C.
Figure 2-11 OLS regression prediction of average hourly mean indoor air temperature and outdoor air temperature of 76 flats
2.3.3 Analysis of thermal comfort and energy savings potential
Thermal comfort as function of indoor and outdoor air temperature
Using Fanger’s model, we calculate PMV and categorize them into the three classes I, II and III following the European standard, EN16789 (see Table 2-5 in the Appendix). We exclude class IV because this class can only be applied to a limited part of the year. Focusing on thermal discomfort when the PPD rate is between 6 and 15%, we investigated 1) whether thermal discomfort is caused by being cold or hot, and 2) how variations of thermal discomfort are affected by outdoor air temperature. Figure 2-12 shows the distribution of different types of thermal discomfort (hot and cold) as a function of outdoor air temperature.
At lower outdoor air temperatures, hot discomfort occurs more frequently (i.e., in more flats) than cold discomfort. This can be explained by the previous finding that temperature
dispersion across flats becomes higher as temperature decreases.
