Power System Load Forecasting Method Based on Recurrent Neural Network

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 a...

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Bibliographic Details
Published in:E3S web of conferences 2020-01, Vol.182, p.2007
Main Authors: Pang, Chuanjun, Bao, Tie, He, Lei
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer architecture
Distributed generation
Electric power systems
Electrical loads
Electricity consumption
Forecasting
Long short-term memory
Mathematical models
Neural networks
Power consumption
Recurrent neural networks
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Summary: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.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202018202007