| Title |
Performance analysis of artificial neural network models for hour-ahead electric load forecasting |
|
Authors |
Velasco, Lemuel Clark P; Arnejo, Karl Anthony S; Macarat, Justine Shane S |
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Publication date |
2022/1/1 |
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Journal |
Procedia Computer Science |
| Volume |
197 |
| Pages |
16-24 |
| Publisher |
Elsevier |
| Abstract |
Supervised Artificial Neural Networks (ANN) is considered as a popular machine learning framework for year-ahead, month-ahead and day-ahead electric load forecasting but is yet to be optimized for very short term predictions like hour-ahead forecasting granularity. This study conducted a performance analysis of ANN models for hour-ahead electric load forecasting that power utility companies can use for agile reaction to its participation among spot markets. Data preparation procedures were conducted which transformed the historical electric load records of a certain geographic area served by a power utility into appropriate forms resulting to partitioned, represented and normalized datasets for ANN training and testing processes. Thirty-six ANN models all having nine input neurons and one output neuron were evaluated and found out that ANN models with Sigmoid activation function exhibited promising forecasting performance shown in terms of Mean Absolute Percentage Error (MAPE) with Back Propagation training algorithm with three hidden neurons having 2.85% MAPE; Quick Propagation training algorithm with six hidden neurons having 2.91% MAPE; and Resilient Propagation training algorithm with six hidden neurons having 3.49% MAPE. The performance analysis presented in this study shows how these ANN models were able to generate close to accurate forecasting results which power utility companies can effectively use for optimal resource management as they participate in spot markets that will require internationally accepted forecasting tolerance error in hour-ahead electric load nomination. |
| Index terms / Keywords |
Artificial neural networks performance analysis hour-ahead load forecasting |
| DOI |
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| URL |
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Mindanao State
University - Iligan Institute of Technology