Abstract
Energy consumption forecasting has known a big interest in the last few years due to its important role in the smart grid domain. It helps to manage and dispatch the smart grid energy sources. Furthermore, load consumption predictions and scheduling of the generation resources to satisfy the demand side enable minimizing the energy generation cost. This paper represents multi-horizons electrical energy forecasting models developed using LSTM, BI-LSTM, unidimensional convolution neural network (1DCNN) and an individual household energy consumption dataset. In addition, a comparative study is exhibited between three forecasting horizons: two hours, four hours, and eight hours. The results show that LTSM and bi-LSTM-based prediction models’ mean squared error (MSE) increases by increasing the prediction horizon. In contrast, 1DCNN based prediction model's MSE decreases by increasing the forecasting horizon.
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Acknowledgment
This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA), and the CNRST of Morocco (Alkhawarizmi/2020/39).
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Jrhilifa, I., Ouadi, H., Jilbab, A. (2023). Multi-horizon Short-Term Load Consumption Forecasting Using Deep Learning Models. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 714. Springer, Cham. https://doi.org/10.1007/978-3-031-35245-4_26
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