Analysis of the Influence of Meteorological Variables on Real-Time Short-Term Load Forecasting in Balearic Islands

被引:11
|
作者
Lopez, M. [1 ]
Valero, S. [1 ]
Senabre, C. [1 ]
Gabaldon, A. [2 ]
机构
[1] Univ Miguel Hernandez, Dept Mech & Energy Elect Engn, Elche, Spain
[2] Univ Politecn Cartagena, Dept Elect Engn, Cartagena, Spain
来源
2017 11TH IEEE INTERNATIONAL CONFERENCE ON COMPATIBILITY, POWER ELECTRONICS AND POWER ENGINEERING (CPE-POWERENG) | 2017年
关键词
Load forecasting; power demand; neural networks applications; NEURAL-NETWORK; WAVELET TRANSFORM;
D O I
10.1109/CPE.2017.7915137
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Short-Term Load Forecasting (STLF) has been a relevant research topic for over two decades now. However, it is an ongoing process since the behavior of consumers and producers continue changing as new technologies and new policies become available. This paper presents the results of a research study for the Spanish Transport System Operator (REE) with the objective of improving the forecasting accuracy in the Balearic Islands. Specifically, this paper will focus on the introduction of meteorological variables other than temperature to reduce forecasting error. The forecasted variables of solar radiation, cloudiness and wind velocity are included in the proposed forecasting model and their effect is analyzed in terms of overall accuracy. Also, a brief analysis of which type of days are actually improved by the use of this new information is presented and used to provide new research guidelines.
引用
收藏
页码:10 / 15
页数:6
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