Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

Shidong Wu, Hengrui Ma (), Abdullah M. Alharbi, Bo Wang, Li Xiong, Suxun Zhu, Lidong Qin and Gangfei Wang
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Shidong Wu: New Energy (Photovoltaic) Industry Research Center, Qinghai University, Xining 810016, China
Hengrui Ma: New Energy (Photovoltaic) Industry Research Center, Qinghai University, Xining 810016, China
Abdullah M. Alharbi: Electrical Department at College of Engineering in Wadi Al-Dawasir, Prince Sattam Bin Abdulaziz University, Wadi Al-Dawasir 11991, Saudi Arabia
Bo Wang: School of Electrical and Automation, Wuhan University, Wuhan 430072, China
Li Xiong: Power Dispatch and Control Center, Guangxi Electric Power Company, Nanning 530013, China
Suxun Zhu: New Energy (Photovoltaic) Industry Research Center, Qinghai University, Xining 810016, China
Lidong Qin: New Energy (Photovoltaic) Industry Research Center, Qinghai University, Xining 810016, China
Gangfei Wang: New Energy (Photovoltaic) Industry Research Center, Qinghai University, Xining 810016, China

Sustainability, 2023, vol. 15, issue 20, 1-23

Abstract: Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve the accuracy of short-term load forecasting, this paper proposes a multi-load forecasting method for integrated energy systems based on the Isolation Forest and dynamic orbit algorithm. First, a high-dimensional data matrix is constructed using the sliding window technique and the outliers in the high-dimensional data matrix are identified using Isolation Forest. Next, the hidden abnormal data within the time series are analyzed and repaired using the dynamic orbit algorithm. Then, the correlation analysis of the multivariate load and its weather data is carried out by the AR method and MIC method, and the high-dimensional feature matrix is constructed. Finally, the prediction values of the multi-load are generated based on the TCN-MMoL multi-task training network. Simulation analysis is conducted using the load data from a specific integrated energy system. The results demonstrate the proposed model’s ability to significantly improve load forecasting accuracy, thereby validating the correctness and effectiveness of this forecasting approach.

Keywords: Isolation Forest; dynamic orbit; MIC; multi-task training; integrated energy systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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