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Communication Dans Un Congrès Année : 2012

Weather-Based Solar Energy Prediction

Marcin Detyniecki
Ashwati Krishnan
  • Fonction : Auteur
Mel Siegel
  • Fonction : Auteur

Résumé

Photovoltaic solar panels are effective energy sources during periods of bright sunlight. Excess energy can be stored for later use at night or on cloudy days. The decision to use the stored energy now or later depends largely on being able to predict the weather on different timescales. Short term prediction of stored energy is challenging due to the non-trivial I-V characteristic of the solar cell. The erratic nature of the weather makes long term predictive energy management difficult. In this paper, we address these issues based on data collected from a solar panel, as well as its relationship to observations made of the weather. We observe that prediction, based on fuzzy decision trees, reduces the energy error by 22% compared to a constant prediction equal to the average on the studied period. Thus, exploiting the fuzzy classification provided by a fuzzy decision tree is a good improvement compared to the baseline.

Dates et versions

hal-01273254 , version 1 (12-02-2016)

Identifiants

Citer

Marcin Detyniecki, Christophe Marsala, Ashwati Krishnan, Mel Siegel. Weather-Based Solar Energy Prediction. The Fuzz-IEEE'2012 Conference, Jun 2012, Brisbane, Australia. pp.587-593, ⟨10.1109/FUZZ-IEEE.2012.6251145⟩. ⟨hal-01273254⟩
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