국립부경대학교 미래 인터넷 및 빅데이터 응용 연구실

Optimizing Models and Data Denoising Algorithms for Power Load Forecasting

Yanxia Li 1,2,Ilyosbek Numonov Rakhimjon Ugli 3,Yuldashev Izzatillo Hakimjon Ugli 2,Taeo Lee 2 andTae-Kook Kim 4,*

Cite This Article

MDPI and ACS Style

Li, Y.; Ugli, I.N.R.; Ugli, Y.I.H.; Lee, T.; Kim, T.-K. Optimizing Models and Data Denoising Algorithms for Power Load Forecasting. Energies 2024, 17, 5513. https://doi.org/10.3390/en17215513

AMA Style

Li Y, Ugli INR, Ugli YIH, Lee T, Kim T-K. Optimizing Models and Data Denoising Algorithms for Power Load Forecasting. Energies. 2024; 17(21):5513. https://doi.org/10.3390/en17215513

Chicago/Turabian Style

Li, Yanxia, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee, and Tae-Kook Kim. 2024. "Optimizing Models and Data Denoising Algorithms for Power Load Forecasting" Energies 17, no. 21: 5513. https://doi.org/10.3390/en17215513

APA Style

Li, Y., Ugli, I. N. R., Ugli, Y. I. H., Lee, T., & Kim, T.-K. (2024). Optimizing Models and Data Denoising Algorithms for Power Load Forecasting. Energies, 17(21), 5513.https://doi.org/10.3390/en17215513

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, Artificial intelligence (AI), big data, Power Forecast

변압기; 데이터 노이즈 감소; Kmeans++; 부하 예측; 클러스터 분석, 인공지능(AI), 빅데이터, 전력 예측

논문 파일 첨부 (pdf file):

Optimizing Models and Data Denoising Algorithms for Power Load Forecasting.pdf