Michel Piliougine

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This paper presents a model for predicting the next-day energy production of a photovoltaic solar plant. The model is capable of forecasting the next-day production profile of such a system, merely by using the information obtained from the plant itself and the solar global radiation values for the previous operation days. This prediction is key in many(More)
We have developed a framework that integrates statistical and machine learning models for the short-term forecasting of a climatic parameter know as the atmospheric clearness index. We have used a multivariate regression to establish the most significant variable amongst all the previous values for the clearness index series. The value of this variable was(More)
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