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Short-term Power Load Forecasting for a 33/11 KV Sub-Station by Utilizing Attention-Based Hybrid Deep Learning Architectures

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Název: Short-term Power Load Forecasting for a 33/11 KV Sub-Station by Utilizing Attention-Based Hybrid Deep Learning Architectures
Autoři: Ram Babu Mukkamala, Venkata Siva Raja Prasad Sunku
Zdroj: Problems of the Regional Energetics, Vol 67, Iss 3, Pp 13-23 (2025)
Informace o vydavateli: Technical University of Moldova, 2025.
Rok vydání: 2025
Témata: TK1001-1841, machine learning, Production of electric energy or power. Powerplants. Central stations, deep learning, TJ807-830, hort-term load, forecasting, Electrical engineering. Electronics. Nuclear engineering, attention-based mechanisms. perfor-mance metrics, Renewable energy sources, TK1-9971
Popis: Estimating electric power load at substations is a fundamental task for system operators, as it is essential for the reliable and optimal operation of the power system. Effective load forecasting is critical for optimal power generation, as precise predictions facilitate the economical use of electrical infrastructure. The primary objective of this study is to develop advanced deep learning (DL) attention-based models aimed at improving the accuracy of short-term electric power load forecasting at substations. This enhancement is essential for ensuring the reliable and efficient operation of power systems. To accomplish this objective, a comprehensive evaluation of various machine learning (ML) and deep learning (DL) architectures was conducted. This evaluation included the following models: Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), Ran-dom Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM) networks with Atten-tion mechanisms, LSTM-Convolutional Neural Network (CNN) Attention, etc. These models applied to hourly estimated energy consumption data (in kilowatts) sourced from the 33/11 kV sub-station in Telangana, India. The performance of these models measured using several key metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The most important result is that the CNN-BiLSTM attention model significantly outperforms the other models, achieving an MSE of 0.0079, an RMSE of 0.0889, and an R² value of 0.8547. that under-scores that the CNN-BiLSTM attention model represents an effective and practical tool for accurate power load forecasting. This capability not only enables the economical utilization of electrical infrastructure but also supports reliable, data-driven decision-making processes within power system operations.
Druh dokumentu: Article
ISSN: 1857-0070
DOI: 10.52254/1857-0070.2025.3-67.02
Přístupová URL adresa: https://doaj.org/article/f074ecd0ea754a1e82071b68f4a65046
Přístupové číslo: edsair.doi.dedup.....93863630bd58b83e8f0891c158628eee
Databáze: OpenAIRE
Popis
Abstrakt:Estimating electric power load at substations is a fundamental task for system operators, as it is essential for the reliable and optimal operation of the power system. Effective load forecasting is critical for optimal power generation, as precise predictions facilitate the economical use of electrical infrastructure. The primary objective of this study is to develop advanced deep learning (DL) attention-based models aimed at improving the accuracy of short-term electric power load forecasting at substations. This enhancement is essential for ensuring the reliable and efficient operation of power systems. To accomplish this objective, a comprehensive evaluation of various machine learning (ML) and deep learning (DL) architectures was conducted. This evaluation included the following models: Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), Ran-dom Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM) networks with Atten-tion mechanisms, LSTM-Convolutional Neural Network (CNN) Attention, etc. These models applied to hourly estimated energy consumption data (in kilowatts) sourced from the 33/11 kV sub-station in Telangana, India. The performance of these models measured using several key metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The most important result is that the CNN-BiLSTM attention model significantly outperforms the other models, achieving an MSE of 0.0079, an RMSE of 0.0889, and an R² value of 0.8547. that under-scores that the CNN-BiLSTM attention model represents an effective and practical tool for accurate power load forecasting. This capability not only enables the economical utilization of electrical infrastructure but also supports reliable, data-driven decision-making processes within power system operations.
ISSN:18570070
DOI:10.52254/1857-0070.2025.3-67.02