| 引用本文: | 陈弘川,蔡 旭,孙国歧,等.基于智能优化方法的相似日短期负荷预测[J].电力系统保护与控制,2021,49(13):121-127.[点击复制] |
| CHEN Hongchuan,CAI Xu,SUN Guoqi,et al.Similar day short-term load forecasting based on intelligent optimization method[J].Power System Protection and Control,2021,49(13):121-127[点击复制] |
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| 摘要: |
| 针对传统相似日法中各因素相似度及其权重需要人工赋值的不足，在充分考虑日期类型、日期距离、气象因素等几种主要常规影响因子的前提下，建立了一种基于智能优化方法的相似日模型对日用电量进行短期预测。相似度计算公式中全部原本需要人工赋值的参数均由历史数据基于果蝇优化算法训练得出。参数值可以根据特定用户的负荷变化特性动态调整，增强了相似日法的准确性和通用性。为了解决求解参数的多维优化问题，避免算法陷入局部极值，提出了一种引入多种群概念的果蝇优化算法，增强了算法的全局搜索能力。仿真实例表明，相比起传统的相似日模型，基于智能优化方法的相似日模型的预测准确率有了明显提高。 |
| 关键词: 短期负荷预测 相似日 参数自适应 果蝇优化算法 |
| DOI：DOI: 10.19783/j.cnki.pspc.201150 |
| 投稿时间：2020-09-18修订日期：2020-12-14 |
| 基金项目:山东省重点研发计划项目资助(2019JZZY020804) |
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| Similar day short-term load forecasting based on intelligent optimization method |
| CHEN Hongchuan1,CAI Xu1,SUN Guoqi2,WEI Xiaobin2,CAO Yunfeng1,SUN Xuefeng2,SU Hui2,ZHANG Lingyan2 |
| (1. Shanghai Jiaotong University, Shanghai 200240, China; 2. Shandong Deyou Electric Co., Ltd., Zibo 255049, China) |
| Abstract: |
| In order to overcome the shortcomings of the traditional similar day method that each factor and its weight need to be manually assigned, this paper establishes a similar day model based on intelligent optimization method to predict daily electricity consumption by fully considering several main conventional influencing factors such as date type, date distance and meteorological factors. All the parameters in the similarity calculation formula that used to be manually assigned are trained from historical data by using fruit fly optimization algorithm. The parameter values can be dynamically adjusted according to the load change characteristics of specific users, which enhances the accuracy and versatility of the similar day method. In order to solve the multi-dimensional optimization problem of parameter training and avoid the algorithm from falling into local extremum, this paper proposes an improved fruit fly optimization algorithm that introduces the concept of multiple groups to enhance the algorithm’s global searching ability. The simulation example shows that compared with the traditional similar day model, the prediction accuracy of the similar day model based on intelligent optimization method has been significantly improved.
This work is supported by the Key Research and Development Program of Shandong Province (No. 2019JZZY020804). |
| Key words: short-term load forecasting similar day parameter adaptation fruit fly algorithm |
