Short-Term Photovoltaic Generation Forecasting Enhanced by Satellite Derived Irradiance
Résumé
In a context of natural resources depletion, weather-dependent renewable energy sources play an increasingly important role in the energy mix. Yet, high shares of renewables can jeopardise the safe operation of power grid due to their variable nature. To address this challenge, it is essential to know the future amount of energy produced to balance production and consumption. This paper aims at investigating photovoltaic generation short-term forecasting and particularly spatio-temporal approaches. These approaches permit to exploit the spatial dependency of weather variables to provide valuable information regarding cloud movements. Thus, it is possible for a power producer to take advantage of dense PV plants networks by considering spatially distributed units as remote sensors. For low-density network, satellite-derived information observed in the vicinity of the power unit location offers an interesting alternative. To reduce the computational burden induced by this data source, feature-selection approaches are implemented. Usually, a correlation score is used to measure the dependence between lagged satellite-based time-series with the target feature (i.e. power production observations). However, this approach tends to provide redundant information (i.e. highly correlated pixels). To address this issue, we implement a minimal-Redundance Maximal-Relevance framework. Performance comparisons with state-of-the-art approaches are also performed.
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