A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability

Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, howe...

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Veröffentlicht in:	Expert systems with applications Jg. 261; S. 125567
Hauptverfasser:	Yang, Qing, Tian, Zhirui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2025
Schlagworte:	Data preprocessing Deep learning Load forecasting Machine learning Multi-objective algorithm Deep learning Load forecasting Multi-objective algorithm Data preprocessing Machine learning
ISSN:	0957-4174
Online-Zugang:	Volltext
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Zusammenfassung:	Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management. •The problem of feature limitation is solved by data dimension extension.•The ability of the optimization algorithm is improved by using multiple improvement strategies.•Ensemble learning using improves the generalization ability of the prediction framework.•Multi-objective optimization algorithm improves the accuracy and stability of prediction.
ISSN:	0957-4174
DOI:	10.1016/j.eswa.2024.125567