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Finding relevant multivariate models for multi-plant photovoltaic energy forecasting

Abstract : Forecasting the photovoltaic energy power is useful for optimizing and controling the system. It aims to predict the power production based on internal and external variables. This problem is very similar to the one of multiple time series forecasting problem. With the presence of multiple predictor variables, not all of them will equally contribute to the prediction. The goal is, given a set of predictors, to find what is the best / most accurate subset (s) leading to the best forecast. In this work, we present a feature selection and model matching framework. The idea is that we try to find the optimal combination of forecasting model with the most relevant features for given variable. We use a variety of causality based selection approaches and dimension reduction techniques. The experiments are conducted on real data and the results advocate the usefulness of the proposed approach.
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Contributor : Alain Casali <>
Submitted on : Monday, January 20, 2020 - 11:45:50 AM
Last modification on : Friday, January 24, 2020 - 2:01:43 AM
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  • HAL Id : hal-02445550, version 1



Youssef Hmamouche, Piotr Przymus, Lotfi Lakhal, Alain Casali. Finding relevant multivariate models for multi-plant photovoltaic energy forecasting. PKDD/ECML, Sep 2017, Skopje, Macedonia. ⟨hal-02445550⟩



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