Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Yi, Lingzhia | Peng, Xinlonga | Fan, Chaodongb | Wang, Yahuia; c; * | Li, Yunfana | Liu, Jiangyonga
Affiliations: [a] College of Automation and Electronic Engineering, Xiangtan University & Hunan Engineering Research Center of Multi-energy Cooperative Control Technology, Xiangtan, Hunan, China | [b] School of Computer Science and Technology, Hainan University, Hainan, China | [c] College of Electrical and Information Engineering, Hunan University, Changsha, China
Correspondence: [*] Corresponding author. Yahui Wang. E-mail: [email protected].
Abstract: Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting.
Keywords: Short-term residential load forecasting, gate recurrent unit, multi-objective optimization algorithm, deep learning
DOI: 10.3233/JIFS-237189
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 10423-10440, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]