| Title |
A Hybrid Model of Autoregressive Integrated Moving Average and Artificial Neural Network for Load Forecasting |
|
Authors |
Velasco, Lemuel Clark; Polestico, Daisy Lou; Macasieb, Gary Paolo; Reye, Michael Bryan; Vasquez, Felicisimo Jr |
|
Publication date |
2019/12 |
|
Journal |
International Journal of Advanced Computer Science and Applications |
| Volume |
10 |
| Issue |
11 |
| Pages |
9 |
| Publisher |
Science and Information |
| 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脙垄脗聙脗聶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脙垄脗聙脗聶s dataset in order to predict the next day脙垄脗聙脗聶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脙垄脗聙脗聶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脙垄脗聙脗聶s electric load data. |
| Index terms / Keywords |
Hybrid model; autoregressive integrated moving average; electric load forecasting; Artificial Neural Network (ANN) |
| DOI |
10.14569/IJACSA.2019.0101103 |
| URL |
|
Mindanao State
University - Iligan Institute of Technology