Performance evaluation of solar energy predictor for wireless sensors

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Submitted on 3 Jul 2019

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Performance evaluation of solar energy predictor for

wireless sensors

Taoufik Bouguera, Jean-François Diouris, Jean-Jacques Chailloux, Guillaume

Andrieux

To cite this version:

Taoufik Bouguera, Jean-François Diouris, Jean-Jacques Chailloux, Guillaume Andrieux. Performance evaluation of solar energy predictor for wireless sensors. Fifth Sino-French Workshop on Information and Communication Technologies (SIFWICT 2019), Jun 2019, Nantes, France. �hal-02167111�

INSTITUT D’ÉLECTRONIQUE ET DE

TÉLÉCOMMUNICATIONS DE RENNES

1

Performance evaluation of solar energy predictor

for wireless sensors

Taoufik Bouguera

1,2

, Jean-François Diouris

1

, Jean-Jacques Chailloux

2

, Guillaume Andrieux

1

1

_{Université de Nantes, CNRS, IETR,}

2

_CEA/LETI

- Wireless sensor nodes are powered by limited energy sources

- Harvesting energy from environment is an alternative

- Solar energy harvesting is an attractive solution

- Available solar power can be predicted

1. Motivations

- Architecture of the proposed system:

2. Context

- Harvested solar energy using 800 mW solar panel in Nantes:

3. Solar energy harvesting

- EWMA (Exponentially Weighted Moving Average):

4. Linear energy prediction model

Day d-1

Day d

- SEPCS (Solar Energy Predictor for Communicating Sensors):

5. Novel real-time prediction

𝑁

_𝑆

𝑁

_𝐽

Previous time-slots

Previous days

.

.

.

6. Performance evaluation

- A novel real-time solar energy prediction is proposed

- The SEPCS algorithm is able to effectively take into account

both the current and the past-days weather conditions

- It can reduce the average of relative error down to only 6.5%

7. Conclusion

- This work is funded by a resourcing project CEA Tech in the

region of Pays de la Loire, in partnership with IETR, CEA Leti

and Eolane.