Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis
Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis
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DOI：
10.1016/j.cie.2012.09.017
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发表时间：
2013-01-01
影响因子：
7.9
通讯作者：
Shakouri, H.
中科院分区：
文献类型：
--
作者：
Kheirkhah, A.;Azadeh, A.;Shakouri, H.
针对用电量的季节变化和月度变化较多，难以用常规方法建模的问题，提出了一种新的算法。提出了一种利用人工神经网络(ANN)、主成分分析(PCA)、数据包络分析(DEA)和方差分析(ANOVA)方法来估计和预测用电量季节性和月度变化的方法。本研究采用了数据挖掘领域中的前处理和后处理技术。分析了数据前处理和后处理对人工神经网络性能的影响，并为此构建了680个ANN-MLP。利用数据包络分析方法对所建立的神经网络模型和神经网络学习算法的性能进行了比较。每个神经网络的平均绝对百分比误差(MAPE)的平均值、最小值、最大值和标准差作为DEA的输入。DEA帮助用户使用适当的ANN模型作为可接受的预测工具。换言之，使用各种误差计算方法来寻找一种稳健的神经网络学习算法。此外，使用主成分分析作为输入选择方法，并从线性(ARIMA)模型和非线性模型中选择一个较好的时间序列模型。在选择首选的ARIMA模型后，应用McLeod-Li检验来确定非线性条件。一旦满足非线性条件，就选择优选的非线性模型并与优选的ARIMA模型进行比较，从而选择最佳的时间序列模型。然后，提出了一种新的时间序列估计算法，在每种情况下都选择神经网络或常规时间序列模型进行估计和预测。为了验证ANN-PCA-DEA-ANOVA算法的适用性和优越性，使用了伊朗1992年4月至2004年2月的用电量数据。结果表明，该算法能较好地解决电力消耗估算问题。(C)2012爱思唯尔有限公司。保留所有权利。
Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod-Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption. (C) 2012 Elsevier Ltd. All rights reserved.