| 引用本文: | 李啸骢,李春涛,从兰美,等.基于动态权值相似日选取算法的短期负荷预测[J].电力系统保护与控制,2017,45(6):1-8. |
| LI Xiaocong,LI Chuntao,CONG Lanmei,et al.Short-term load forecasting based on dynamic weight similar day selection algorithm[J].Power System Protection and Control,2017,45(6):1-8 |
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| 摘要: |
| 提出了一种基于动态权值优化的相似日选取算法和灰色GRNN串联组合模型的短期负荷预测方法。采用动态权值相似日选取算法，在考虑不同地区和季节对短期负荷的影响时，引入改进的果蝇优化算法(MFOA)，动态调整各因子的权值，增强了相似日选取算法的适应性和有效性。选取出相似日后，采用灰色模型和广义回归神经网络(GRNN)串联组合的短期负荷预测方法，并通过改进的布谷鸟(MCS)算法对GRNN平滑因子进行优化，组合模型改善了单一模型预测精度的稳定性。实例预测结果验证了该方法的有效性。 |
| 关键词: 短期负荷预测 相似日 改进的果蝇优化算法 灰色模型 广义回归神经网络 改进的布谷鸟算法 |
| DOI：10.7667/PSPC160412 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(51267001);广西科学研究与技术开发计划项目(14122006-29);广西自然科学基金资助项目(2014GXNSFAA118338) |
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| Short-term load forecasting based on dynamic weight similar day selection algorithm |
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LI Xiaocong,LI Chuntao,CONG Lanmei,REN Ziyi,LUO Hongliang,WANG Yuwen,YUAN Hui,QIU Hao
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(College of Electrical Engineering, Guangxi University, Nanning 530004, China)
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| Abstract: |
| A short-term load forecasting method based on similar day algorithm with dynamic weight value and GM- GRNN model is proposed. Traditional ways to select similar days can not effectively identify the dominant factors. Aiming at this problem, the dynamic weight similarity day selection algorithm based on modified fruit fly optimization algorithm (MFOA) is proposed, which is based on the optimization algorithm of different regions and seasons. The method improves adaptability and effectiveness of the algorithm. Moreover, the accuracy of load forecasting is enhanced. After the selection of similar days, the short-term load forecasting method of grey model and generalized regression neural network (GRNN) is proposed, which improves the stability of the single model forecasting accuracy. In order to further optimize the prediction model, the GRNN smoothing factor is optimized by the modified cuckoo search (MCS) algorithm. Simulation results verify the validity of the proposed method. This work is supported by National Natural Science Foundation of China (No. 51267001), Guangxi Scientific Research and Technological Development Program (No. 14122006-29), and Natural Science Foundation of Guangxi Province (No. 2014GXNSFAA118338). |
| Key words: short-term load forecasting similar day fruit fly algorithm grey model general regression neural network modified cuckoo search |
