1Renewable Energy Res Ctr, China Electric Power Research Institute, Beijing 100192, China
2State Grid Xinjiang Electric Power Science Research Institute, Wulumuqi 830063, China
3School of Energy and Power Engineering, North China Electric Power University, Beijing 100096, China
BibTex Citation Data :
@article{IJRED60632, author = {Weisheng Wang and Wenhui Shi and Dongliang Nan and Yinzhang Peng and Qinghua Wang and Yankai Zhu}, title = {New energy output prediction and demand response optimization based on LSTM-BN}, journal = {International Journal of Renewable Energy Development}, volume = {14}, number = {1}, year = {2025}, keywords = {New energy; Long and short-term memory network; Bayesian network; Price-based demand response strategy}, abstract = { The study proposed a new energy output prediction model based on long and short-term memory network (LSTM)-Bayesian network (BN) by combining the benefits of BN in uncertainty quantification with the processing power of LSTM network to address the issue of volatility and uncertainty of new energy output. Meanwhile, by introducing a price-based demand response mechanism, users were incentivized to increase electricity consumption when the new energy generation was in excess and reduce electricity consumption during the peak period, so as to realize the flexible regulation of loads and the efficient utilization of new energy. The new energy output prediction model developed in the study had the highest degree of match between the anticipated and actual values in various data sets, as demonstrated by the experimental findings, which were above 0.99. In the Google Earth Engin and GEFCom2014 datasets, the operation solution speed was quick and stabilized after 64 and 80 iterations, respectively. Additionally, the model’s predicted and actual curve values almost matched, and the actual new energy output power predication's largest prediction error was less than 1%. The implementation of a price-based demand response approach to control customers' power consumption behavior yielded a net benefit of up to 4.45 million yuan for the customers in the target area, based on the precise prediction of new energy output power. The aforementioned findings demonstrated that the LSTM-BN-based new energy output prediction model is capable of precisely projecting new energy output and efficiently matching supply and demand through a price-based demand response mechanism to increase the rate at which new energy is consumed instantly. }, pages = {72--82} doi = {10.61435/ijred.2025.60632}, url = {https://ijred.cbiore.id/index.php/ijred/article/view/60632} }
Refworks Citation Data :
The study proposed a new energy output prediction model based on long and short-term memory network (LSTM)-Bayesian network (BN) by combining the benefits of BN in uncertainty quantification with the processing power of LSTM network to address the issue of volatility and uncertainty of new energy output. Meanwhile, by introducing a price-based demand response mechanism, users were incentivized to increase electricity consumption when the new energy generation was in excess and reduce electricity consumption during the peak period, so as to realize the flexible regulation of loads and the efficient utilization of new energy. The new energy output prediction model developed in the study had the highest degree of match between the anticipated and actual values in various data sets, as demonstrated by the experimental findings, which were above 0.99. In the Google Earth Engin and GEFCom2014 datasets, the operation solution speed was quick and stabilized after 64 and 80 iterations, respectively. Additionally, the model’s predicted and actual curve values almost matched, and the actual new energy output power predication's largest prediction error was less than 1%. The implementation of a price-based demand response approach to control customers' power consumption behavior yielded a net benefit of up to 4.45 million yuan for the customers in the target area, based on the precise prediction of new energy output power. The aforementioned findings demonstrated that the LSTM-BN-based new energy output prediction model is capable of precisely projecting new energy output and efficiently matching supply and demand through a price-based demand response mechanism to increase the rate at which new energy is consumed instantly.
Article Metrics:
Last update:
Last update: 2025-02-08 16:54:35
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Articles are freely available to both subscribers and the wider public with permitted reuse.
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options: Creative Commons Attribution-ShareAlike (CC BY-SA). Authors and readers can copy and redistribute the material in any medium or format, as well as remix, transform, and build upon the material for any purpose, even commercially, but they must give appropriate credit (cite to the article or content), provide a link to the license, and indicate if changes were made. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
International Journal of Renewable Energy Development (ISSN:2252-4940) published by CBIORE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.