Research Article
A Neural Network-Based Model for Predicting Electricity Revenue in the Context of New Energy
@INPROCEEDINGS{10.4108/eai.8-12-2023.2344761, author={Li Wu and Yongjun Chen and Bo Li and Liangyu Zou}, title={A Neural Network-Based Model for Predicting Electricity Revenue in the Context of New Energy}, proceedings={Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8--10, 2023, Guangzhou, China}, publisher={EAI}, proceedings_a={MSIEID}, year={2024}, month={4}, keywords={big data; electricity fee prediction; medium and long-term electricity market; spot market; assisting decision-making}, doi={10.4108/eai.8-12-2023.2344761} }- Li Wu
Yongjun Chen
Bo Li
Liangyu Zou
Year: 2024
A Neural Network-Based Model for Predicting Electricity Revenue in the Context of New Energy
MSIEID
EAI
DOI: 10.4108/eai.8-12-2023.2344761
Abstract
Through big data analysis and modeling research on short-term and long-term electricity bill prediction in the power market, the results show that exponential smoothing method, ARMA model, maximum information entropy model, and "association rule+ARMA" models are suitable for short-term electricity bill prediction; State space models and neural network models are more suitable for long-term electricity bill prediction. By using predictive models, it has a certain guiding role in providing transaction assistance decision-making for the medium to long term electricity market and spot electricity energy market.
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