Solar radiation forecasting using ad-hoc time series preprocessing and neural networks

2009/06/01 by Christophe Paoli, Cyril Voyant, Paoli, Christophe +5
Computer Science · Energy · Engineering · #Artificial Intelligence (cs.AI) #Data Analysis #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Numerical Analysis (math.NA) #Photovoltaic System Optimization Techniques #Solar Radiation and Photovoltaics #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.0906.0311

openalex publication_date 2009/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Artificial intelligence techniques for photovoltaic applications: A review

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