Multi-Energy Coupling Load Forecasting in Integrated Energy System with Improved Variational Mode Decomposition-Temporal Convolutional Network-Bidirectional Long Short-Term Memory Model

Xinfu Liu (), Wei Liu, Wei Zhou, Yanfeng Cao, Mengxiao Wang, Wenhao Hu, Chunhua Liu, Peng Liu and Guoliang Liu
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Xinfu Liu: Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Ministry of Education), Qingdao University of Technology, Qingdao 266520, China
Wei Liu: Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Ministry of Education), Qingdao University of Technology, Qingdao 266520, China
Wei Zhou: CNOOC Research Institute Ltd., Beijing 100028, China
Yanfeng Cao: CNOOC Research Institute Ltd., Beijing 100028, China
Mengxiao Wang: CNOOC Research Institute Ltd., Beijing 100028, China
Wenhao Hu: Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Ministry of Education), Qingdao University of Technology, Qingdao 266520, China
Chunhua Liu: College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao 266580, China
Peng Liu: Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Ministry of Education), Qingdao University of Technology, Qingdao 266520, China
Guoliang Liu: Key Lab of Industrial Fluid Energy Conservation and Pollution Control (Ministry of Education), Qingdao University of Technology, Qingdao 266520, China

Sustainability, 2024, vol. 16, issue 22, 1-18

Abstract: Accurate load forecasting is crucial to the stable operation of integrated energy systems (IES), which plays a significant role in advancing sustainable development. Addressing the challenge of insufficient prediction accuracy caused by the inherent uncertainty and volatility of load data, this study proposes a multi-energy load forecasting method for IES using an improved VMD-TCN-BiLSTM model. The proposed model consists of optimizing the Variational Mode Decomposition (VMD) parameters through a mathematical model based on minimizing the average permutation entropy (PE). Moreover, load sequences are decomposed into different Intrinsic Mode Functions (IMFs) using VMD, with the optimal number of models determined by the average PE to reduce the non-stationarity of the original sequences. Considering the coupling relationship among electrical, thermal, and cooling loads, the input features of the forecasting model are constructed by combining the IMF set of multi-energy loads with meteorological data and related load information. As a result, a hybrid neural network structure, integrating a Temporal Convolutional Network (TCN) with a Bidirectional Long Short-Term Memory (BiLSTM) network for load prediction is developed. The Sand Cat Swarm Optimization (SCSO) algorithm is employed to obtain the optimal hyper-parameters of the TCN-BiLSTM model. A case analysis is performed using the Arizona State University Tempe campus dataset. The findings demonstrate that the proposed method can outperform six other existing models in terms of Mean Absolute Percentage Error (MAPE) and Coefficient of Determination ( R 2 ), verifying its effectiveness and superiority in load forecasting.

Keywords: BiLSTM; integrated energy system; multi-energy load forecasting; TCN; VMD (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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