A New Strategy for Short-Term Load Forecasting
Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2013/208964 |
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| _version_ | 1832561696721338368 |
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| author | Yi Yang Jie Wu Yanhua Chen Caihong Li |
| author_facet | Yi Yang Jie Wu Yanhua Chen Caihong Li |
| author_sort | Yi Yang |
| collection | DOAJ |
| description | Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF. |
| format | Article |
| id | doaj-art-3c6743d68f43423b997640b9a3d07635 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-3c6743d68f43423b997640b9a3d076352025-02-03T01:24:23ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/208964208964A New Strategy for Short-Term Load ForecastingYi Yang0Jie Wu1Yanhua Chen2Caihong Li3School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, ChinaSchool of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, ChinaElectricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.http://dx.doi.org/10.1155/2013/208964 |
| spellingShingle | Yi Yang Jie Wu Yanhua Chen Caihong Li A New Strategy for Short-Term Load Forecasting Abstract and Applied Analysis |
| title | A New Strategy for Short-Term Load Forecasting |
| title_full | A New Strategy for Short-Term Load Forecasting |
| title_fullStr | A New Strategy for Short-Term Load Forecasting |
| title_full_unstemmed | A New Strategy for Short-Term Load Forecasting |
| title_short | A New Strategy for Short-Term Load Forecasting |
| title_sort | new strategy for short term load forecasting |
| url | http://dx.doi.org/10.1155/2013/208964 |
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