Comparison of Artificial Intelligence Techniques for Energy Consumption Estimation

被引：0
|
作者：
Olanrewaju, Oludolapo Akanni [1 ]
Mbohwa, Charles [1 ]
机构：
[1] Univ Johannesburg, Fac Engn & Built Environm, Ind & Operat Dept, Johannesburg, South Africa
来源：
2016 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC) | 2016年
关键词：
Energy consumption; Multilayer perceptron; Radial basis function; Support vector regression; NEURAL-NETWORKS; RADIAL BASIS; PREDICTION;
D O I：
暂无
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 article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing data from 1990 to 2000. Comparisons were based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Square Error (RRSE) as well as Simulation Time. The best results were obtained for the Multilayer Perceptron.
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页数：5
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