Medium-Term Electricity Demand Forecasting Based on MARS

被引：0

作者：

Ilseven, Engin ^{[1
]}

Gol, Murat ^{[1
]}

机构：

[1] Middle East Tech Univ, Dept Elect & Elect Engn, Ankara, Turkey

来源：

2017 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE EUROPE (ISGT-EUROPE) | 2017年

关键词：

Multiple Linear Regression; Generalized Additive Models; Multivariate Adaptive Regression Splines; Artificial Neural Networks; Electricity Demand Forecasting; ADAPTIVE REGRESSION SPLINES; POWER SYSTEMS; LOAD;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper proposes use of multivariate adaptive regression splines (MARS) method to perform monthly electricity demand forecasting for medium-term. The model is developed based on specific example of Turkey; however is applicable to any other system. Performance of the proposed method is compared to that of multiple linear regression (MLR), generalized additive model (GAM), and artificial neural networks (ANN) methods. The validation process shows that the proposed model outperforms the other ones by test error and shows stable error performance.

引用

页数：6

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