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http://dx.doi.org/10.12673/jant.2019.23.6.561

Prediction of Short and Long-term PV Power Generation in Specific Regions using Actual Converter Output Data  

Ha, Eun-gyu (Department of Energy IT, Gachon University)
Kim, Tae-oh (Department of IT Convergence Engineering, Gachon University)
Kim, Chang-bok (Department of Energy IT, Gachon University)
Publication Information
Journal of Advanced Navigation Technology / v.23, no.6, 2019 , pp. 561-569 More about this Journal
Abstract
Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.
Keywords
Photovoltaic; linear regression; Support vector machine; Deep neural network; Recurrent neural network;
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