Predicting the output of a PV plant - Cissé, Abdou Aziz; Diallo, Mamadou Salif
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Energy market players (investors, power producers, grid operators, consumers, etc.) are facing potential challenges such as the growing demand for energy, new patterns of energy consumption, the integration of (intermittent) renewable energy sources into power grids and the evolution of power grids.This book investigates the possibility of predicting the production of a self-consuming photovoltaic installation by artificial neural networks. We cross-compared two neural network architectures (looped and unlooped) with respect to multivariate regression in order to have an efficient and reliable…mehr

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Produktbeschreibung
Energy market players (investors, power producers, grid operators, consumers, etc.) are facing potential challenges such as the growing demand for energy, new patterns of energy consumption, the integration of (intermittent) renewable energy sources into power grids and the evolution of power grids.This book investigates the possibility of predicting the production of a self-consuming photovoltaic installation by artificial neural networks. We cross-compared two neural network architectures (looped and unlooped) with respect to multivariate regression in order to have an efficient and reliable tool for predicting the production of a PV installation based on meteorological data (sunshine and ambient temperature).To do so, we used monitoring data of a plant over a 72-day period to build, train and test two neural network topologies (looped and unlooped) which are trained with the Levenberg-Marquardt algorithm.
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Autorenporträt
Ingeniero de investigación especializado en energías renovables y sistemas inteligentes. Licenciado en Tecnología Solar Aplicada por la Universidad de Uagadugú (Burkina Faso) y con un Máster de Investigación por la Escuela Politécnica de Thiès (Senegal). Áreas de investigación: energías renovables - sistemas inteligentes - IA y aprendizaje automático.