Combined time series model based on DQN for short-term load forecasting

TianXiao Hang; BoYu Pei; ZhiQiang Dai

doi:10.1117/12.2674815

2 May 2023 Combined time series model based on DQN for short-term load forecasting

TianXiao Hang, BoYu Pei, ZhiQiang Dai

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422B (2023) https://doi.org/10.1117/12.2674815
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Accurate short-term load forecasting has important guiding significance for power grid planning and operation. Most of the current mainstream prediction models are single prediction models, which are prone to noise interference and lead to poor performance. In order to give full play to the advantages of different models, a combined time series model based on a deep reinforcement learning algorithm is proposed. First, five-time series models are established respectively, and two dominant models, Bi-LSTM and Bi-GRU, are screened out according to performance indicators. Secondly, the DQN algorithm is used to optimize the weight coefficient of the output result of the combined model to improve the prediction accuracy. Finally, this paper uses Australian electrical load data for example analysis. The experimental results show that the prediction performance of the proposed combined model is better than that of the benchmark single model, and it has certain practicability.

Citation Download Citation

TianXiao Hang, BoYu Pei, and ZhiQiang Dai "Combined time series model based on DQN for short-term load forecasting", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422B (2 May 2023); https://doi.org/10.1117/12.2674815

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