Optimizing Models and Data Denoising Algorithms for Power Load Forecasting

Yanxia Li, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee and Tae-Kook Kim ()
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Yanxia Li: Department of Computer Science, Linfen Vocational and Technical College, Linfen 041000, China
Ilyosbek Numonov Rakhimjon Ugli: Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea
Yuldashev Izzatillo Hakimjon Ugli: Department of Computer Engineering, Pukyong National University, Busan 48513, Republic of Korea
Taeo Lee: Department of Computer Engineering, Pukyong National University, Busan 48513, Republic of Korea
Tae-Kook Kim: School of Computer and Artificial Intelligence Engineering, Pukyong National University, Busan 48513, Republic of Korea

Energies, 2024, vol. 17, issue 21, 1-16

Abstract: To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, and uses Kmeans++ for clustering processing. On this basis, a Transformer model based on an adversarial adaptive mechanism is designed, which aligns the data distribution of the source domain and target domain through a domain discriminator and feature extractor, thereby reducing the impact of domain offset on prediction accuracy. The mean square error of the Fourier transform clustering method used in this study was 0.154, which was lower than other methods and had a better data denoising effect. In load forecasting, the mean square errors of the model in predicting long-term load, short-term load, and real-time load were 0.026, 0.107, and 0.107, respectively, all lower than the values of other comparative models. Therefore, the load forecasting model designed for research has accuracy and stability, and it can provide a foundation for the precise control of urban power systems. The contributions of this study include improving the accuracy and stability of the load forecasting model, which provides the basis for the precise control of urban power systems. The model tracks periodicity, short-term load stochasticity, and high-frequency fluctuations in long-term loads well, and possesses high accuracy in short-term, long-term, and real-time load forecasting.

Keywords: transformer; data noise reduction; Kmeans++; load forecasting; cluster analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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