Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network

Hai Lan, Chi Zhang, Ying-Yi Hong, Yin He and Shuli Wen

Applied Energy, 2019, vol. 247, issue C, 389-402

Abstract: Owing to a shortage of fossil fuels, environmental pollution and the greenhouse effect, renewable energy generation has become important in a modern smart grid. However, the characteristics of renewable power generation are volatile and uncertain. This work proposes a new day-ahead spatiotemporal forecasting method for solar irradiation to ensure the efficient operation of power systems. First, the frequency features of a solar irradiation time-series are extracted by discrete Fourier transform (DFT). Principal component analysis (PCA) is then used to identify the crucial frequency features, which are input to an Elman-based neural network to carry out subsequent 24-hour (day ahead) solar irradiation forecasting. Historical weather data from five weather stations that are near the target location are used. Comparative studies of traditional Autoregressive Integrated Moving Average Model (ARIMA), PCA-Back-Propagation (BP)-based neural network, the persistence method, DFT-PCA-BP and the proposed DFT-PCA-Elman method reveal that the proposed method is the most accurate in day-ahead forecasting using spatiotemporal data.

Keywords: Artificial neural network; Discrete Fourier transform; Renewable energy; Spatiotemporal forecasting; Principal component analysis (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (26)

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DOI: 10.1016/j.apenergy.2019.04.056

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