Abstract
Electricity load forecasting plays a vital role in planning power systems in any region. A reliable forecast model is essential to implement an affordable and sustainable energy system. Several studies reveal that weather conditions in a region have a strong relation with energy generation. This paper presents deep learning models to forecast electricity demand considering both energy generation and weather factors in a region. The deep learning models consist of LSTM, stacked LSTM, and CNN-LSTM. In addition, ARIMAX forecasting is applied to compare the effectiveness of the deep learning models. With RRMSE of 1.528%, the CNN-LSTM model outperforms not only the other LSTM models but also ARIMAX confirming the usability of the proposed model.
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Zaman, S., Nayeem, M., Tatrapi, R., Ripon, S. (2022). Deep Learning Approach for Electricity Load Forecasting Using Multivariate Time Series Data. In: Skala, V., Singh, T.P., Choudhury, T., Tomar, R., Abul Bashar, M. (eds) Machine Intelligence and Data Science Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-19-2347-0_62
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