Adaptive electricity consumption forecasting approach for universal environments
Abstract The development of an accurate electricity consumption forecast model is crucial for stable operation and intelligent management of power systems. Traditional methods often overlook user heterogeneity and lack measures to address concept drift caused by distribution changes in electricity d...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10147-2 |
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| _version_ | 1849343916246040576 |
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| author | Shiqi Zhou Saisai Ni Yifeng Han Zhekang Dong Chun Sing Lai |
| author_facet | Shiqi Zhou Saisai Ni Yifeng Han Zhekang Dong Chun Sing Lai |
| author_sort | Shiqi Zhou |
| collection | DOAJ |
| description | Abstract The development of an accurate electricity consumption forecast model is crucial for stable operation and intelligent management of power systems. Traditional methods often overlook user heterogeneity and lack measures to address concept drift caused by distribution changes in electricity data over time. We propose an adaptive electricity consumption probability forecasting method tailored to universal environments. The method includes a nonmonotonic correlation elimination-based recursive feature selection that adaptively determines the optimal feature combination. Our model incorporates a joint loss function combining point and probability forecasting evaluations to accurately quantify online batch errors. It also features a buffer to store batch data showing pattern changes and dynamically adjusts weights to counteract concept drift. We validated our method, adaptive electricity consumption forecast for universal environments (AECF-UC), against some mainstream methods using a multi-environment dataset. Comparative and ablation experiments show that AECF-UC outperforms others, achieving average RMSE, pinball loss and CRPS of 0.3041, 0.0567 and 0.1683 respectively, with the joint loss method improving prediction accuracy by about 6% over the single-loss method. These results indicate that the proposed method exhibits certain advantages in universality and adaptability. |
| format | Article |
| id | doaj-art-de05e8d27ecd451b9dd8bd939836cdbc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-de05e8d27ecd451b9dd8bd939836cdbc2025-08-20T03:42:49ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-10147-2Adaptive electricity consumption forecasting approach for universal environmentsShiqi Zhou0Saisai Ni1Yifeng Han2Zhekang Dong3Chun Sing Lai4College of Electronic Information, Hangzhou Dianzi UniversityCollege of Electronic Information, Hangzhou Dianzi UniversityCollege of Electronic Information, Hangzhou Dianzi UniversityCollege of Electronic Information, Hangzhou Dianzi UniversityDepartment of Electronic and Computer Engineering, Brunel UniversityAbstract The development of an accurate electricity consumption forecast model is crucial for stable operation and intelligent management of power systems. Traditional methods often overlook user heterogeneity and lack measures to address concept drift caused by distribution changes in electricity data over time. We propose an adaptive electricity consumption probability forecasting method tailored to universal environments. The method includes a nonmonotonic correlation elimination-based recursive feature selection that adaptively determines the optimal feature combination. Our model incorporates a joint loss function combining point and probability forecasting evaluations to accurately quantify online batch errors. It also features a buffer to store batch data showing pattern changes and dynamically adjusts weights to counteract concept drift. We validated our method, adaptive electricity consumption forecast for universal environments (AECF-UC), against some mainstream methods using a multi-environment dataset. Comparative and ablation experiments show that AECF-UC outperforms others, achieving average RMSE, pinball loss and CRPS of 0.3041, 0.0567 and 0.1683 respectively, with the joint loss method improving prediction accuracy by about 6% over the single-loss method. These results indicate that the proposed method exhibits certain advantages in universality and adaptability.https://doi.org/10.1038/s41598-025-10147-2Electricity consumption forecastingConcept driftProbability forecastingHidden Markov model |
| spellingShingle | Shiqi Zhou Saisai Ni Yifeng Han Zhekang Dong Chun Sing Lai Adaptive electricity consumption forecasting approach for universal environments Scientific Reports Electricity consumption forecasting Concept drift Probability forecasting Hidden Markov model |
| title | Adaptive electricity consumption forecasting approach for universal environments |
| title_full | Adaptive electricity consumption forecasting approach for universal environments |
| title_fullStr | Adaptive electricity consumption forecasting approach for universal environments |
| title_full_unstemmed | Adaptive electricity consumption forecasting approach for universal environments |
| title_short | Adaptive electricity consumption forecasting approach for universal environments |
| title_sort | adaptive electricity consumption forecasting approach for universal environments |
| topic | Electricity consumption forecasting Concept drift Probability forecasting Hidden Markov model |
| url | https://doi.org/10.1038/s41598-025-10147-2 |
| work_keys_str_mv | AT shiqizhou adaptiveelectricityconsumptionforecastingapproachforuniversalenvironments AT saisaini adaptiveelectricityconsumptionforecastingapproachforuniversalenvironments AT yifenghan adaptiveelectricityconsumptionforecastingapproachforuniversalenvironments AT zhekangdong adaptiveelectricityconsumptionforecastingapproachforuniversalenvironments AT chunsinglai adaptiveelectricityconsumptionforecastingapproachforuniversalenvironments |