Optimizing loss functions for improved energy demand prediction in smart power grids
Nussipova F. Rysbekov S. Abdiakhmetova Z. Kartbayev A.
June 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 33415 - 3426 pp.
In this paper, our aim is to improve the accuracy and effectiveness of energy demand forecasting, particularly within modern electricity transmission systems and smart grid technology. To achieve this, we developed a hybrid approach that combines machine learning, representation learning, and other deep learning techniques. This approach is based on extracting essential features, including time-based attributes, identifiable trends, and optimal lags. The outcome of our investigation is the observation that triplet losses demonstrate remarkable accuracy, particularly when employed with a larger margin size and for longer prediction lengths. This finding signifies a substantial improvement in the precision and reliability of energy demand forecasting within modern electricity transmission systems. Our research not only improves predictive modeling in the power grid but also demonstrates the practical use of advanced analytics in addressing renewable energy integration challenges, refining energy demand forecasting for efficient management, system operation, and market analysis.
Latent features , Neural networks , Power grid , Representation learning , Triplet loss
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School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, Kazakhstan
Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan
School of Information Technology and Engineering
Faculty of Information Technology
10 лет помогаем публиковать статьи Международный издатель
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