 Data augmentation for improving heating load prediction of heating substation based on TimeGANYunfei Zhang, Zhihua Zhou, Junwei Liu and Jianjuan Yuan Energy, 2022, vol. 260, issue C Abstract: Heating load predictions serve as one of the fundamental tasks in heating operation management. Many studies have used data-driven methods to build prediction models, and the quantity and quality of training data are key factors affecting the model performance. However, for some special cases, such as new heating substation and the end period of heating with different load characteristics, sufficient and high-quality data cannot be provided for model training, resulting in low accuracy of the model. In this paper, TimeGAN is applied in the heating field for the first time to augment the data and improve the prediction accuracy of the model. Results show that the prediction error reduces by 50% and CV-RMSE can reach 0.0405 after using TimeGAN in the early period of heating, and the accuracy is highest when the synthetic data are three times of original data. For the mid and end period of heating, the prediction errors can also be reduced by 3%–8% compared with training on original data, and the data amount reaches 15,000–30000, the performance of the model reaches the best. Keywords: Heating load; Prediction model; High-quality data; TimeGAN (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018205 DOI: 10.1016/j.energy.2022.124919 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.