Solar photovoltaic power (PV) generation has increased constantly in several countries in the last ten years becoming an important component of a sustainable solution of the energy problem. In this paper, a methodology to 24 h or 48 h photovoltaic power forecasting based on a Neural Network, trained in a Bayesian framework, is proposed. More specifically, a multi-ahead prediction Multi-Layer Perceptron Neural Network is used, whose parameters are estimated by a probabilistic Bayesian learning technique. The Bayesian framework allows obtaining the confidence intervals and to estimate the error bars of the Neural Network predictions. In order to build an effective model for PV forecasting, the time series of Global Horizontal Irradiance, Cloud Cover, Direct Normal Irradiance, 2-m Temperature, azimuth angle and solar Elevation Angle are used and preprocessed by a Linear Predictive Coding technique. The experimental results show a low percentage of forecasting error on test data, which is encouraging if compared to state-of-the-art methods in literature.

A bayesian-based neural network model for solar photovoltaic power forecasting

CIARAMELLA, Angelo
;
STAIANO, Antonino;
2016

Abstract

Solar photovoltaic power (PV) generation has increased constantly in several countries in the last ten years becoming an important component of a sustainable solution of the energy problem. In this paper, a methodology to 24 h or 48 h photovoltaic power forecasting based on a Neural Network, trained in a Bayesian framework, is proposed. More specifically, a multi-ahead prediction Multi-Layer Perceptron Neural Network is used, whose parameters are estimated by a probabilistic Bayesian learning technique. The Bayesian framework allows obtaining the confidence intervals and to estimate the error bars of the Neural Network predictions. In order to build an effective model for PV forecasting, the time series of Global Horizontal Irradiance, Cloud Cover, Direct Normal Irradiance, 2-m Temperature, azimuth angle and solar Elevation Angle are used and preprocessed by a Linear Predictive Coding technique. The experimental results show a low percentage of forecasting error on test data, which is encouraging if compared to state-of-the-art methods in literature.
9783319337463
9783319337463
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/54787
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