Neural network learning and expert systems
Neural network learning and expert systems
Performance of neural networks in managerial forecasting
International Journal of Intelligent Systems in Accounting and Finance Management - Special issue on neural networks
Neural networks in applied statistics
Technometrics
Neural network models for time series forecasts
Management Science
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Expert Systems with Applications: An International Journal
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Expert Systems with Applications: An International Journal
An intelligent fast sales forecasting model for fashion products
Expert Systems with Applications: An International Journal
A new prediction strategy for price spike forecasting of day-ahead electricity markets
Applied Soft Computing
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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.