Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation

• Published in: Energy (Oxford) 2011-01, Vol.36 (1), p.348-359
• Main Authors: Voyant, Cyril, Muselli, Marc, Paoli, Christophe, Nivet, Marie-Laure
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial neural networks; Computer Science; Energy; Energy prediction; Exact sciences and technology; meteorological data; Natural energy; Neural and Evolutionary Computing; neural networks; prediction; Processing; PV plant; Solar energy; Solar radiation; Stationarity; summer; time series analysis; Time series forecasting; winter
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2010.10.032

This paper presents an application of Artificial Neural Networks (ANNs) to predict daily solar radiation. We look at the Multi-Layer Perceptron (MLP) network which is the most used of ANNs architectures. In previous studies, we have developed an ad-hoc time series preprocessing and optimized a MLP with endogenous inputs in order to forecast the solar radiation on a horizontal surface. We propose in this paper to study the contribution of exogenous meteorological data (multivariate method) as time series to our optimized MLP and compare with different forecasting methods: a naïve forecaster (persistence), ARIMA reference predictor, an ANN with preprocessing using only endogenous inputs (univariate method) and an ANN with preprocessing using endogenous and exogenous inputs. The use of exogenous data generates an nRMSE decrease between 0.5% and 1% for two stations during 2006 and 2007 (Corsica Island, France). The prediction results are also relevant for the concrete case of a tilted PV wall (1.175 kWp). The addition of endogenous and exogenous data allows a 1% decrease of the nRMSE over a 6 months-cloudy period for the power production. While the use of exogenous data shows an interest in winter, endogenous data as inputs on a preprocessed ANN seem sufficient in summer. ► Use of exogenous data as ANN inputs to forecast horizontal daily global irradiation data. ► New methodology allowing to choice the adequate exogenous data – a systematic method comparing endogenous and exogenous data. ► Different referenced mathematical predictors allows to conclude about the pertinence of the proposed methodology.