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Title Load Forecasting using Autoregressive Integrated Moving Average and Artificial Neural Network
Posted by Lemuel Clark Velasco
Authors Velasco, Lemuel Clark; Polestico, Daisy Lou; Macasieb, Gary Paolo; Reyes, Michael Bryan; Vasquez, Felicisimo Jr.
Publication date 2018/8/1
Journal International Journal of Advanced Computer Science and Applications IJACSA
Volume 9
Issue 7
Pages 23-29
Publisher The Science and Information (SAI) Organization
Abstract Electric load forecasting is a challenging research problem due to the complicated nature of its dataset involving both linear and nonlinear properties. Various literatures attempted to develop forecasting models that utilized statistical in combination with machine learning approaches deal with the dataset’s linear and nonlinear components to obtain close to accurate predictions. In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as forecasting models for a power utility’s dataset in order to predict day-ahead electric load. Electric load data preparation, models implementation and forecasting evaluation was conducted to assess if the prediction of the models met the acceptable error tolerance for day-ahead electric load forecasting. A Java-based system made use of R Statistical Software implemented ARIMA(8,1,2) while Encog Library was used to implement the ANN model composing of Resilient Propagation as the training algorithm and Hyperbolic Tangent as the activation function. The ANN+ARIMA hybrid model was found out to deliver a Mean Absolute Percentage Error (MAPE) of 4.09% which proves to be a viable technique in electric load forecasting while showing better forecasting results than solely using ARIMA and ANN. Through this research, both statistical and machine learning approaches were implemented as a forecasting model combination to solve the linear and non-linear properties of electric load data.
Index terms / Keywords Electric load forecasting; autoregressive integrated moving average; artificial neural network
DOI 10.14569/IJACSA.2018.090704