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Title Hour-Ahead Electric Load Forecasting Using Artificial Neural Networks
Posted by Lemuel Clark Velasco
Authors Velasco, Lemuel Clark P; Arnejo, Karl Anthony S; Macarat, Justine Shane S; Tinam-isan, Mia Amor C
Publication date 2022
Chapter of the book Lecture Notes in Networks and Systems
Volume 216
Pages 843-855
Publisher Springer, Singapore
Abstract To ensure an efficient supply of electricity, power utility companies make use of a combination of either short-term, medium-term, and long-term forecasting techniques. This paper presents a strategy for implementing hour-ahead electricity load forecasting using artificial neural networks (ANN). Data preparation through data selection, cleaning, partitioning, and transformation was performed to the dataset provided by a power utility in Southern Philippines. ANN models using a feedforward architecture having 9 input neurons, 6 hidden neurons, and 1 output neuron with a learning rate set to 0.00001, momentum set to 0.7 with an epoch value of 20,000, and a maximum error of 0.001 was implemented using a Java-based open-source library. Results showed that the ANN model with a quick propagation training algorithm and sigmoid activation function had a Mean Absolute Percentage Error (MAPE) of 2.91%, and the ANN model with resilient propagation training algorithm and sigmoid activation function had a MAPE of 3.49%. This study shows that with appropriate data preparation and machine learning implementation, ANN has the potential to aid the decision-making of power utility companies through short-term forecasting in terms of hour-ahead electric load consumption prediction.
Index terms / Keywords Artificial neural networks, Hour-ahead electric load forecasting, Short-term electric load forecasting