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Solar Radiation Forecasting Using Ad-Hoc Time Series Preprocessing and Neural Networks

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Article , Preprint , Conference object 01 Jan 2009Embargo end date: 01 Jan 2009 France Publisher:Springer Berlin Heidelberg

Authors: Paoli, Christophe; Voyant, Cyril; Muselli, Marc; Nivet, Marie Laure;

doi: 10.1007/978-3-642-04070-2_95 , 10.48550/arxiv.0906.0311

arXiv: http://arxiv.org/abs/0906.0311 , 0906.0311

Solar Radiation Forecasting Using Ad-Hoc Time Series Preprocessing and Neural Networks

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Abstract

In this paper, we present an application of neural networks in the renewable energy domain. We have developed a methodology for the daily prediction of global solar radiation on a horizontal surface. We use an ad-hoc time series preprocessing and a Multi-Layer Perceptron (MLP) in order to predict solar radiation at daily horizon. First results are promising with nRMSE < 21% and RMSE < 998 Wh/m2. Our optimized MLP presents prediction similar to or even better than conventional methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors approximators. Moreover we found that our data preprocessing approach can reduce significantly forecasting errors.

14 pages, 8 figures, 2009 International Conference on Intelligent Computing

Country

France

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Keywords

FOS: Computer and information sciences, Computer Science - Artificial Intelligence, [SCCO.COMP]Cognitive science/Computer science, FOS: Physical sciences, Time Series, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [SPI.NRJ] Engineering Sciences/Electric power, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [ SPI.NRJ ] Engineering Sciences [physics]/Electric power, [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-NE] Computer Science/Neural and Evolutionary Computing, FOS: Mathematics, Multi-Layer, Mathematics - Numerical Analysis, [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Preprocessing, Perceptron, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Seasonality, Numerical Analysis (math.NA), Artificial Intelligence (cs.AI), [ SCCO.COMP ] Cognitive science/Computer science, Physics - Data Analysis, Statistics and Probability, [INFO.INFO-AI] Computer Science/Artificial Intelligence, Data Analysis, Statistics and Probability (physics.data-an)

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	39
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%