Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.
Bibliographical noteFunding Information:
This research was supported by the Energy Cloud R&D Program (grant number: 2019M3F2A1073184) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
© 2023 by the authors.
- complete ensemble empirical mode decomposition with adaptive noise
- renewable energy sources
- smart grid
- solar radiation forecasting
- wavelet transform
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)