Abstract: The complementary strengths and weaknesses of both statistical modeling paired with machine learning has been an ongoing technique in the development and implementation of forecasting models that analyze the dataset’s linear as well as nonlinear components in the generation of accurate prediction results. In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as a hybrid forecasting model for a power utility’s dataset in order to predict the next day’s electric load consumption. ARIMA and ANN models were serially developed resulting to the findings that out of the twelve evaluated ARIMA models, ARIMA (8,1,2) exhibited the best forecasting performance. After identifying the optimal ANN layers and input neurons, this study showed that out of the six evaluated supervised feedforward ANN models, the ANN model which employed Hyperbolic Tangent activation function and Resilient Propagation training algorithm also exhibited the best forecasting performance. With Zhang’s ARIMA and ANN hybridization technique, this study showed that the hybrid model delivered Mean Absolute Percentage Error (MAPE) of 4.09% which is within the 5% internationally accepted forecasting error for electric load forecasting. Through the findings of this research, both the ARIMA statistical model and ANN machine learning approaches showed promising results in being implemented as a forecasting model pair to analyze the linear as well as non-linear properties of a power utility’s electric load data.

Lemuel Clark P Velasco, Daisy Lou L. Polestico, Gary Paolo O. Macasieb, Michael Bryan V. Reyes and Felicisimo B. Vasquez Jr, “A Hybrid Model of Autoregressive Integrated Moving Average and Artificial Neural Network for Load Forecasting” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101103

@article{Velasco2019,
title = {A Hybrid Model of Autoregressive Integrated Moving Average and Artificial Neural Network for Load Forecasting},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2019.0101103},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101103},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
author = {Lemuel Clark P Velasco and Daisy Lou L. Polestico and Gary Paolo O. Macasieb and Michael Bryan V. Reyes and Felicisimo B. Vasquez Jr},
}