Short-Term Load Forecasting using Artificial Neural Networks and Multiple Linear Regression

被引：0
|
作者：
Govender, Sahil [1 ]
Folly, Komla A. [1 ]
机构：
[1] Univ Cape Town, Dept Elect Engn, Cape Town, South Africa
来源：
2019 IEEE PES/IAS POWERAFRICA | 2019年
关键词：
Artificial intelligence; artificial neural networks; multiple linear regression; short term load forecasting;
D O I：
10.1109/powerafrica.2019.8928857
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：
In this paper, two methods for short-term load forecasting are compared; namely, artificial neural networks (ANNs) and multiple linear regression (MLR). Only input features that had a very large correlation with the load were used. Historic load data are shown to have the strongest correlation with the current load data than other weather variables such as temperature and humidity. Simulation results show that the MLR give better results for the seasonal forecasts, whereas the ANN showed an overall lower mean absolute percentage error (MAPE) for the daily forecasts.
引用
收藏
页码：273 / 278
页数：6
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