Improving Short-Term Electrical Load Forecasting with Dilated Convolutional Neural Networks: A Comparative Analysis
DOI:
https://doi.org/10.18196/jrc.v6i2.24967Keywords:
Dilated Convolution, Load Forecasting, Deep Learning, Energy Management, Time SeriesAbstract
Short-term load forecasting (STLF) is vital for grid stability and resource optimization for energy systems. Accurate forecasting helps maintain a stable power supply, reduce costs, and improve decision-making. Traditional convolutional neural networks (CNNs) capture local patterns well but struggle with long-term dependencies under fluctuating conditions. This study introduces an optimized Dilated Convolutional Neural Network (DCNN) to enhance accuracy in short- and long-term load forecasting. The key contribution is a new DCNN framework that expands the receptive field without adding computational complexity, effectively capturing multi-level temporal dependencies. This improves performance, stability, and accuracy in volatile conditions. The methodology applies dilated convolution techniques to a real-world electricity load dataset with 13,440 hourly data points. Preprocessing includes normalization and outlier removal. Hyperparameter tuning optimizes dilation rates, kernel sizes, and learning rates. Results show that the DCNN outperforms traditional models, achieving the lowest Mean Absolute Percentage Error (MAPE) of 0.0096. These results surpass CNN (MAPE: 0.0116), GRU (MAPE: 0.0102), and Long Short-Term Memory (LSTM) (MAPE: 0.0272) models. The DCNN also maintains efficiency and stability with volatile data. In conclusion, optimized dilated convolution techniques significantly enhance load forecasting, offering scalable, robust solutions for modern energy management systems requiring fast, accurate, and reliable predictions.
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