Abstract
Power load forecasting is becoming the key role in nowadays power distribution networks to understand the behavior of the electrical power systems where end users can predict the future trends of electricity usage and hence manage their residential appliances to reduce high electrical bills. Some researchers used traditional methods that cannot handle the problem of large-scale nonlinear time series data. The main objective of this paper is to conduct various optimized deep learning models for power load forecasting and presents the best performing model with high accuracy and the lowest possible Mean-Square Error by fine-tuning the parameters to achieve the best possible configurations of the model. Using hybrid Recurrent Neural Network techniques, optimal results have been achieved. The research was conducted on Long-short term memory, Long-short term memory encoder-decoder, Convolutional Neural Network Long-short term memory, Gated Recurrent Units, Convolutional Long-short term memory, and Bidirectional Long-short term memory. The training process was conducted using Google Colaboratory and the results showed that Convolutional Long-short term memory has the lowest Root-Mean Square Error among the 6 Long-short term memory networks time series forecasting models. The prediction plot of prediction and real values against epochs are almost inline.
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Chang, KC., Turatsinze, E., Zheng, J., Chang, FH., Wang, HC., Amesimenu, G.D.K. (2022). Long-Short Term Memory Model with Univariate Input for Forecasting Individual Household Electricity Consumption. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_12
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