A Very Short-term Load Forecasting Method Based on Deep LSTM RNN at Zone Level
(2.Shandong Power Dispatching & Control Center, Ji’nan, Shandong Province, China 250000)
【Abstract】Very short-term load forecasting provides a basis for a real-time electricity market. Improvement of its forecasting accuracy is important for revealing the uncertainties of the load and the deviations of day-ahead load forecasting results. Based on big data resources in power systems, a deep long short-term memory (LSTM) load forecasting method at a small area level is proposed, including preprocessing of input data, construction of a deep LSTM network, and search for hyper-parameters with the random search method. Under such hyper-parameters, comparison of forecasting performance between the proposed method and machine-learning method is conducted. Results prove that the proposed method can achieve better forecasting accuracy, which is suitable for offline training and online predicting. Also, through visualization of hidden-layer activation units and calculation of correlation, the ability of feature learning and correlation mining of a deep LSTM network is proven.
【Keywords】 very short-term load forecasting; deep LSTM; recurrent neural network (RNN) ; visualization; correlation analysis;
(Translated by HAN R)
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