.png){kind=icon}
返回
Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting
.png){kind=icon}
PRE
AI
DOI：10.1109/TPWRS.2010.2052638.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China.
Keyword:
Adaptive method
deviation forecasting
secondary forecasting
short-term load forecasting
support vector machine (SVM)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
分享收藏
Parallel neural network-fuzzy expert system strategy for short-term load forecasting: System implementation and performance evaluation短期负荷预测的并行神经网络-模糊专家系统策略: 系统实现和性能评估
PREAI
分享收藏
查看更多内容