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Short-Term Load Forecasting Reliability in Power Plant of Cyber-Physical Energy System Considering Adaptive Denoising | IEEE Journals & Magazine | IEEE Xplore

Short-Term Load Forecasting Reliability in Power Plant of Cyber-Physical Energy System Considering Adaptive Denoising


Abstract:

Cyber-physical energy systems (CPES) are a crucial component of smart grids (SGs), and as such, they represent a specialized subset of cyber-physical systems. CPES provid...Show More

Abstract:

Cyber-physical energy systems (CPES) are a crucial component of smart grids (SGs), and as such, they represent a specialized subset of cyber-physical systems. CPES provides essential services for pricing decisions and automatic generation control through short-term load forecasting (STLF), making the accuracy of STLF critical to optimizing their operation. However, due to the numerous communication devices installed within CPES, data collection is often subject to various factors that could negatively impact load forecasting accuracy. To improve the accuracy of STLF, this article proposes a reliable method that combines an adaptive denoising technique, a 2-D deep temporal convolutional network (TDeepTCN), and a multidimensional input structure bidirectional long short-term memory-attention (MBiLSTM-attention) network. First, an adaptive approach that combines Pearson correlation coefficient and complete ensemble empirical mode decomposition with adaptive noise is utilized to effectively identify raw load series contaminated by noise and reconstruct them. Then, a TDeepTCN model is constructed using TCN to simultaneously capture and fuse both local and long-term temporal features from multiple load series. Finally, MBiLSTM-attention is employed for accurate forecasting to achieve feature processing for multidimensional depth features. Eventually, compared to existing models, our proposed model achieves the most accurate forecasting results with a mean absolute percentage error rate of only 3.98% and 4.12%, respectively, in both regions.
Published in: IEEE Systems Journal ( Volume: 17, Issue: 4, December 2023)
Page(s): 5183 - 5194
Date of Publication: 07 September 2023

ISSN Information:

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I. Introduction

With the deep integration of smarter sensing components and more complex communication and control networks in the power system, it has changed prominently toward future power system, also called cyber-physical energy system (CPES), which is combined power networks and cyber-physical systems (CPS), as shown in Fig. 1. The supply and demand balance between power suppliers and customers is the fundamental driving force for the development of CPES. Therefore, accurate load forecasting, especially short-term load forecasting (STLF), will play a key role in CPES by providing power suppliers with data support for the coordination of supply and demand, as well as automatic electricity generation control in modern power systems [1], [2].

References

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