中国电机工程学报Issue(22)：5715-5722,8.DOI:10.13334/j.0258-8013.pcsee.2015.22.005
基于神经网络的日峰荷预测方法中日期类型系数的确定
Weekday Index Determination for ANN-Based Daily Peak Load Forecasting Method
摘要
Abstract
For the ANN-based short-term load forecasting, the weekday index (day of week) is a very important influencing factor that needs to be considered. Usually, the weekday index is coded as 7 binary inputs. This paper proposed a method that codes the weekday index as only one input variable. Due to the simplicity, the precision of the forecasting was improved. The proposed weekday index is determined by estimating the load’s difference of different day types. For this purpose, the regression curves of load versus temperature of different day types were calculated. To eliminate the accumulation effect and get a clearer weekly pattern of these regression curves, a genetic-algorithm (GA) based method was adopted to modify the temperature variables. The proposed method was demonstrated by the calculated results on the load data of the Suzhou city in China.关键词
负荷预测/神经网络/日期类型系数/气温累积效应Key words
load forecasting/neural network/weekday index/accumulation effect分类
信息技术与安全科学引用本文复制引用
包宇庆,李扬,杨斌,陈楚,阮文骏..基于神经网络的日峰荷预测方法中日期类型系数的确定[J].中国电机工程学报,2015,(22):5715-5722,8.基金项目
国家自然科学基金项目(71471036，51277028)；江苏省2014年度普通高校研究生科研创新计划项目(KYLX_0123)；国网科技项目(SGJS0000YXWT1400641)。Project Supported by National Natural Science Foundation of China (71471036,51277028) （71471036，51277028）
2014 Annual General University Graduate Research and Innovation Project of Jiangsu province (KYLX_0123) （KYLX_0123）
Science and Technology Project of State Grid (SGJS0000YXWT1400641) （SGJS0000YXWT1400641）
