Artificial neural network based short term electrical load forecasting

• Main Authors: Oon, Yi Her, Mahmud, Mohd. Saiful Azimi, Zainal Abidin, Mohamad Shukri, Ayop, Razman, Buyamin, Salinda
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/99459/1/MohdSaifulAzimi2022_ArtificialNeuralNetworkBasedShortTerm.pdf; http://eprints.utm.my/id/eprint/99459/; http://dx.doi.org/10.11591/ijpeds.v13.i1.pp586-593

In power generation, a 24-hour load profile can vary significantly throughout the day. Therefore, power generation must be adjusted to reduce money loss due to excess generation. This paper presents a short-term load forecasting (STLF) system design using artificial neural network (ANN). As ANN come in many different configurations, this paper analyzes the best ANN configuration via trial-and-error method. To train the ANN, historical load data from 2016 to 2018 of power south energy cooperative (AEC) is used. A simple feedforward ANN type with one hidden layer is implemented, where 48 neurons are used at the input layer. For hidden layer, an arbitrary 50 neurons are chosen and 24 neurons at output layer are used to generate a day ahead 24-hour load profile. To measure the best activation function for SLTF application, four non-linear activation functions will be tested and the best activation function is used to create two and three hidden layer ANN architecture. Finally, the performance of the two new networks will be compared against one hidden layer model. From the obtained result, the best performing model is found as two hidden layers ANN with Tanh as its hidden layer activation function with 8.9% of testing mean absolute percentage error (MAPE).