Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet

Lihe Liang; Jinying Cui; Juanjuan Zhao; Yan Qiang; Qianqian Yang; Lihe Liang; Jinying Cui; Juanjuan Zhao; Yan Qiang; Qianqian Yang

doi:10.3934/mbe.2024150

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 3391-3421. doi: 10.3934/mbe.2024150

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Research article

Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet

1.
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030600, China
2.
School of Software, Taiyuan University of Technology, Taiyuan 030600, China
3.
College of Information, Jinzhong College of Information, Jinzhong 030800, China

Academic Editor: Yang Kuang

Received: 18 December 2023 Revised: 15 January 2024 Accepted: 16 January 2024 Published: 04 February 2024

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.
- ultra-short-term time series prediction,
- series decomposition,
- global trend features,
- local seasonal features,
- quantile probabilistic load forecasting
Citation: Lihe Liang, Jinying Cui, Juanjuan Zhao, Yan Qiang, Qianqian Yang. Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 3391-3421. doi: 10.3934/mbe.2024150

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Abstract

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

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