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。
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.
作者
陈弘川
蔡旭
孙国歧
魏晓宾
曹云峰
孙学锋
苏辉
张玲艳
CHEN Hongchuan;CAI Xu;SUN Guoqi;WEI Xiaobin;CAO Yunfeng;SUN Xuefeng;SU Hui;ZHANG Lingyan(Shanghai Jiaotong University,Shanghai 200240,China;Shandong Deyou Electric Co.,Ltd.,Zibo 255049,China)
出处
《电力系统保护与控制》
CSCD
北大核心
2021年第13期121-127,共7页
Power System Protection and Control
基金
山东省重点研发计划项目资助(2019JZZY020804)。
关键词
短期负荷预测
相似日
参数自适应
果蝇优化算法
short-term load forecasting
similar day
parameter adaptation
fruit fly algorithm
