Abstract
Electricity prediction helps electric power companies to generate sufficient electrical power to consumers. The primary source used in performing forecasting is historical electricity usage. This research identified the optimum historical load data period in generating the best model for short-term forecasting of a household. The experiment applied Long Short-Term Memory (LSTM) architecture using Adaptive Learning Rate Method (Adadelta) on four categories of dataset: one-year, two-years, three-years, and four-years. The models produced were evaluated using mean squared error (MSE) and mean absolute error (MAE). The model generated from two-years of historical data performed the best among all other models with MSE value of 0.133621 and MAE value of 0.050653. The experiment was enclosed with the application of the model to predict the electricity usage of the following year, shown in two sample categories: one day and one week. Then, the prediction results were compared with the actual load.
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Acknowledgements
The publication of this paper was funded by URND TNB Seeding Fund: U-TE-RD-20-08. The authors would like to thank the Institute of Informatics and Computing in Energy (IICE), Universiti Tenaga Nasional (UNITEN) for providing a platform to collaborate with the Center for Energy Informatics, Southern Denmark University (SDU).
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Salleh, N.S.M., Suliman, A., Jørgensen, B.N. (2022). Comparison of Electricity Usage Forecasting Model Evaluation Based on Historical Load Dataset Duration Using Long Short-Term Memory Architecture. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_51
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