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August 1, 2025Problems of the Regional EnergeticsOpen Access

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

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Authors

RMRambabu MukkamalaVSVenkata Siva Raja Prasad Sunku

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Overview

This analysis demonstrates improved power load forecasting accuracy in substations, suggesting deep learning enhances operational efficiency.

Key Points

  • The CNN-BiLSTM attention model significantly improves forecasting accuracy, achieving an MSE of 0.0079.
  • Key metrics include RMSE of 0.0889 and an R² value of 0.8547, indicating strong model performance.
  • Evaluation includes various models like ARIMA, MLP, and LSTM to compare forecasting accuracy.
  • Results imply efficient power system operations can benefit from advanced machine learning techniques.

Cite This Study

Mukkamala et al. (2025) studied this question.

synapsesocial.com/papers/68af431bad7bf08b1ead1b92https://doi.org/10.52254/1857-0070.2025.3-67.02
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Also Consider

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  1. 1A Comparative Study of Hybridized Machine Learning Models for Short-Term Load Prediction in Medium-Voltage Electricity Networks2026 · 1 citations
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  3. 3Short-term power load forecasting of a city in Henan Province using Attention based LTSM2024
  4. 4Short Term Load Forecasting for Smart Distribution System Planning Using Deep Neural Networks: A Hybrid Approach2024 · 1 citations
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