Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm
With the rapid development of the power industry, the power load prediction is becoming more and more important in recent years, and short-term load prediction plays an extremely important role in dispatching and market operation of the power system. Power load prediction can effectively improve the...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2021-03-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202011132 |
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| author | Haihong FAN |
| author_facet | Haihong FAN |
| author_sort | Haihong FAN |
| collection | DOAJ |
| description | With the rapid development of the power industry, the power load prediction is becoming more and more important in recent years, and short-term load prediction plays an extremely important role in dispatching and market operation of the power system. Power load prediction can effectively improve the utilization of power generation equipment. The selective ensemble learning method based on Kappa statistic and the glowworm swarm optimization algorithm (GSO) to forecast short-term load is proposed. This proposed method firstly generates multiple learners by bootstrap sampling, and then use glowworm swarm optimization algorithm to select some learners with large differences and high accuracy to participate in the integration. Compared with a single learner, the accuracy of the proposed method is significantly improved. The daily average load curves of two enterprises in Wuhan from 2015 to 2016 are used as a case study to carry out load forecasting. Comparing with other forecasting methods, the prediction accuracy of the proposed method is proved to be higher. |
| format | Article |
| id | doaj-art-092a38cd771845d48ee8ddd5e11cd64c |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2021-03-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-092a38cd771845d48ee8ddd5e11cd64c2025-08-20T02:57:32ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492021-03-0154314114810.11930/j.issn.1004-9649.202011132zgdl-54-4-fanhaihongShort-Term Power Load Forecasting Method based on Glowworm Swarm Optimization AlgorithmHaihong FAN0China Energy Materials Company Limited, Beijing 100055, ChinaWith the rapid development of the power industry, the power load prediction is becoming more and more important in recent years, and short-term load prediction plays an extremely important role in dispatching and market operation of the power system. Power load prediction can effectively improve the utilization of power generation equipment. The selective ensemble learning method based on Kappa statistic and the glowworm swarm optimization algorithm (GSO) to forecast short-term load is proposed. This proposed method firstly generates multiple learners by bootstrap sampling, and then use glowworm swarm optimization algorithm to select some learners with large differences and high accuracy to participate in the integration. Compared with a single learner, the accuracy of the proposed method is significantly improved. The daily average load curves of two enterprises in Wuhan from 2015 to 2016 are used as a case study to carry out load forecasting. Comparing with other forecasting methods, the prediction accuracy of the proposed method is proved to be higher.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202011132short-term power load forecastingglowworm swarm optimization algorithmselective ensemble learningmeteorological factorforecasting model |
| spellingShingle | Haihong FAN Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm Zhongguo dianli short-term power load forecasting glowworm swarm optimization algorithm selective ensemble learning meteorological factor forecasting model |
| title | Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm |
| title_full | Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm |
| title_fullStr | Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm |
| title_full_unstemmed | Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm |
| title_short | Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm |
| title_sort | short term power load forecasting method based on glowworm swarm optimization algorithm |
| topic | short-term power load forecasting glowworm swarm optimization algorithm selective ensemble learning meteorological factor forecasting model |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202011132 |
| work_keys_str_mv | AT haihongfan shorttermpowerloadforecastingmethodbasedonglowwormswarmoptimizationalgorithm |