Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study

Muhammad Aslam, Jae-Myeong Lee, Hyung-Seung Kim, Seung-Jae Lee and Sugwon Hong
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Muhammad Aslam: Department of Electrical Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea
Jae-Myeong Lee: Department of Computer Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea
Hyung-Seung Kim: Department of Electrical Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea
Seung-Jae Lee: Department of Electrical Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea
Sugwon Hong: Department of Computer Engineering, Myongji University, Yongin, Gyeonggi 17058, Korea

Energies, 2019, vol. 13, issue 1, 1-15

Abstract: Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency.

Keywords: deep learning; microgrid; renewable energy; solar radiation forecasting; gated recurrent unit; long short term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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