Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM

• URL: http://arxiv.org/abs/2406.06651v1
• Date: Mon, 10 Jun 2024 09:02:07 GMT
• Title: Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM
• Authors: Kazi Fuad Bin Akhter, Sadia Mobasshira, Saief Nowaz Haque, Mahjub Alam Khan Hesham, Tanvir Ahmed,
• Abstract summary: This paper proposes a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city. The proposed approach produced the best prediction results in comparison to the other benchmark models used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092.
• Score: 0.471858286267785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The precise forecasting of electricity demand also referred to as load forecasting, is essential for both planning and managing a power system. It is crucial for many tasks, including choosing which power units to commit to, making plans for future power generation capacity, enhancing the power network, and controlling electricity consumption. As Bangladesh is a developing country, the electricity infrastructure is critical for economic growth and employment in this country. Accurate forecasting of electricity demand is crucial for ensuring that this country has a reliable and sustainable electricity supply to meet the needs of its growing population and economy. The complex and nonlinear behavior of such energy systems inhibits the creation of precise algorithms. Within this context, this paper aims to propose a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city. Short-term forecasting is ordinarily done to anticipate load for the following few hours to a few weeks. Normalization techniques have been also investigated because of the sensitivity of these models towards the input range. The proposed approach produced the best prediction results in comparison to the other benchmark models (LSTM, CNN- BiLSTM and CNN-LSTM) used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092. The result of the proposed model also outperformed some of the existing works on load-forecasting.

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