E3S Web Conf.
Volume 182, 20202020 10th International Conference on Power, Energy and Electrical Engineering (CPEEE 2020)
|Number of page(s)||6|
|Section||Modern Power System Control and Operation|
|Published online||31 July 2020|
Power System Load Forecasting Method Based on Recurrent Neural Network
NARI Group Co., Ltd. (State Grid Electric Power Research Institute Co., Ltd.), China, 100192
* Corresponding author: firstname.lastname@example.org
Power system load forecasting plays an important role in the power dispatching operation. The development of the electricity market and the increasing integration of distributed generators have increased the complexity of power consumption model and put forward higher requirements for the accuracy and stability of load forecasting. A load forecasting method based on long-short term memory (LSTM) is proposed. This method uses deep recurrent neural network from the artificial intelligence field to establish a load forecasting model. Using the LSTM network to memorize the long-term dependence of the sequence data, the intrinsic variation of the load itself is identified from both the horizontal and vertical dimensions within a longer historical time period, while considering various influencing factors. Actual load data is used to verify the forecasting performance of different historical date windows and different network architectures.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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