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http://dx.doi.org/10.12673/jant.2018.22.3.233

Short Term Forecast Model for Solar Power Generation using RNN-LSTM  

Shin, Dong-Ha (Department of Energy IT, Gachon University)
Kim, Chang-Bok (Department of Energy IT, Gachon University)
Publication Information
Journal of Advanced Navigation Technology / v.22, no.3, 2018 , pp. 233-239 More about this Journal
Abstract
Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.
Keywords
Solar power generation forecasting; Deep learning; Artificial neural network; Sunshine; Solar radiation;
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Times Cited By KSCI : 7  (Citation Analysis)
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