| 摘要: |
| 针对智能用电环境下负荷随机性强、短期电力负荷预测精度差、计算时间长等问题，提出了一种结合改进果蝇优化算法IFOA和广义回归神经网络GRNN的预测方法。模型的输入因子为负荷数据和气象信息等。通过改进果蝇优化算法的搜索距离，增强其搜索能力，优化广义回归神经网络 的平滑因数，提高预测的网络性能和精度。通过仿真验证预测方法的准确性和有效性。结果表明，改进后的方法可以减小预测误差，提高算法的稳定性。该研究为我国电力负荷预测的发展提供了参考和借鉴。 |
| 关键词: 电力负荷预测 果蝇优化算法 广义回归神经网络 平滑因数 |
| DOI：10.19783/j.cnki.pspc.190760 |
| 投稿时间：2019-07-01修订日期：2019-08-12 |
| 基金项目:国家十三五重点研发项目资助(2017YFB0602500) |
|
| Research on short-term power load forecasting method based on IFOA-GRNN |
| ZHU Xuechang |
| (Department of Equipment Engineering, Henan Technical College of Construction, Zhengzhou 450064, China) |
| Abstract: |
| Examining the problems of strong load randomness, poor short-term load forecasting accuracy and long calculation time in intelligent power environment, a forecasting method combining the improved Drosophila optimization algorithm IFOA and generalized regression neural network GRNN is proposed. The input factors of the model are load data and meteorological information. By improving the search distance of the fruit fly optimization algorithm, the model can be used to optimize the smooth factor of the generalized regression neural network GRNN, thereby improving the performance and prediction accuracy of the network. The accuracy and validity of the proposed prediction method are verified by simulation. The results show that the improved method can reduce prediction error and increase the stability of the algorithm. This study provides a reference for the development of a short-term power load forecasting system in China. This work is supported by National “Thirteen-Five Year” Research and Development Program of China (No. 2017YFB0602500). |
| Key words: power load forecasting fruit fly optimization algorithm generalized regression neural network smoothing factor |
