Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism
Short-term load forecasting (STLF) plays a vital role in the reliable, secure, and efficient operation of power systems. Since electric load variation results from diverse factors, accurate and stable load forecasting remains a challenging task. To increase the forecasting accuracy and stability, in...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9212410/ |
| _version_ | 1818853465601867776 |
|---|---|
| author | Pan Zeng Min Jin Md. Fazla Elahe |
| author_facet | Pan Zeng Min Jin Md. Fazla Elahe |
| author_sort | Pan Zeng |
| collection | DOAJ |
| description | Short-term load forecasting (STLF) plays a vital role in the reliable, secure, and efficient operation of power systems. Since electric load variation results from diverse factors, accurate and stable load forecasting remains a challenging task. To increase the forecasting accuracy and stability, in this paper, we newly propose a short-term load forecasting method based on the cross multi-model and second decision mechanism. First, we combine horizontal and longitudinal training set selection method to construct the cross training sets, which acquire both the horizontal and longitudinal characteristics of the load variation. Second, to improve the generalization ability and extend the application scope, we construct forecasting multi-models by training multiple forecasting algorithms with cross training sets. Finally, to aggregate the forecasting outputs obtained by the forecasting multi-models, we propose a second decision mechanism based on a decision multi-model and adaptive weight allocation strategy, which overcomes the limited learning ability shortcoming of single decision models and further improves the forecasting accuracy. Case studies based on electrical load data from the state of Maine, the region of New England, Singapore, and New South Wales of Australia show that both the accuracy and the stability of the proposed method are superior to the compared models. |
| first_indexed | 2024-12-19T07:37:15Z |
| format | Article |
| id | doaj.art-68e0ee9310044caca662adf967b37a5f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:37:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-68e0ee9310044caca662adf967b37a5f2022-12-21T20:30:33ZengIEEEIEEE Access2169-35362020-01-01818406118407210.1109/ACCESS.2020.30286499212410Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision MechanismPan Zeng0https://orcid.org/0000-0002-6674-9749Min Jin1Md. Fazla Elahe2College of Computer Science and Electronics Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronics Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronics Engineering, Hunan University, Changsha, ChinaShort-term load forecasting (STLF) plays a vital role in the reliable, secure, and efficient operation of power systems. Since electric load variation results from diverse factors, accurate and stable load forecasting remains a challenging task. To increase the forecasting accuracy and stability, in this paper, we newly propose a short-term load forecasting method based on the cross multi-model and second decision mechanism. First, we combine horizontal and longitudinal training set selection method to construct the cross training sets, which acquire both the horizontal and longitudinal characteristics of the load variation. Second, to improve the generalization ability and extend the application scope, we construct forecasting multi-models by training multiple forecasting algorithms with cross training sets. Finally, to aggregate the forecasting outputs obtained by the forecasting multi-models, we propose a second decision mechanism based on a decision multi-model and adaptive weight allocation strategy, which overcomes the limited learning ability shortcoming of single decision models and further improves the forecasting accuracy. Case studies based on electrical load data from the state of Maine, the region of New England, Singapore, and New South Wales of Australia show that both the accuracy and the stability of the proposed method are superior to the compared models.https://ieeexplore.ieee.org/document/9212410/Short-term load forecastingmulti-modelcross training set, second decision mechanismmodel aggregation |
| spellingShingle | Pan Zeng Min Jin Md. Fazla Elahe Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism IEEE Access Short-term load forecasting multi-model cross training set, second decision mechanism model aggregation |
| title | Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism |
| title_full | Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism |
| title_fullStr | Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism |
| title_full_unstemmed | Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism |
| title_short | Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism |
| title_sort | short term power load forecasting based on cross multi model and second decision mechanism |
| topic | Short-term load forecasting multi-model cross training set, second decision mechanism model aggregation |
| url | https://ieeexplore.ieee.org/document/9212410/ |
| work_keys_str_mv | AT panzeng shorttermpowerloadforecastingbasedoncrossmultimodelandseconddecisionmechanism AT minjin shorttermpowerloadforecastingbasedoncrossmultimodelandseconddecisionmechanism AT mdfazlaelahe shorttermpowerloadforecastingbasedoncrossmultimodelandseconddecisionmechanism |