A Multi-level Artificial Neural Network for Residential and Commercial Energy Demand Forecast: Iran Case Study

被引：0

作者：

Kazemi, A. ^{[1
]}

Shakouri, H. G. ^{[2
]}

Menhaj, M. B. ^{[3
]}

Mehregan, M. R. ^{[1
]}

Neshat, N. ^{[4
]}

机构：

[1] Univ Tehran, Fac Management, Tehran, Iran

[2] Univ Tehran, Dept Ind Engn, Tehran, Iran

[3] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran

[4] Sharif Univ Technol, Dept Ind Engn, Tehran, Iran

来源：

INFORMATION AND FINANCIAL ENGINEERING, ICIFE 2011 | 2011年 / 12卷

关键词：

ANN; MLP; BP algorithm; Forecasting; Energy demand; Residential and commercial sectors; CONSUMPTION;

D O I：

暂无

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

This paper presents a neuro-based approach for Iran annual residential and commercial energy demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the residential and commercial energy demand, gross domestic product (GDP), total number of households, energy prices and investment for construction are selected. This approach is structured as a multi-level artificial neural network (ANN) based on supervised multi-layer perceptron (MLP), trained with the back-propagation (BP) algorithm. This multi-level ANN is designed properly. This paper indeed proposed a multi-level network by which the inputs to the ending level are obtained as outputs of the starting levels. Actual data of Iran from 1967-2007 is used to train the multi-level ANN and illustrate capability of the approach in this regard. Comparison of the model predictions with data of the evaluation stage shows validity of the model. Furthermore, the energy demand for the period of 2008 to 2020 is estimated.

引用

页码：25 / 29

页数：5

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