Multienergy Load Forecasting for Regional Integrated Energy Systems Considering Multienergy Coupling of Variation Characteristic Curves

Wang, Shouxiang; Wu, Kaixin; Zhao, Qida; Wang, Shaomin; Liang, Feng; Zheng, Zemin; Guo, Wei

doi:10.3389/fenrg.2021.635234

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…For instance, Literature (Guo et al, 2022) developed a multi-energy load forecasting model based on MTL and Bi-directional Long Short-Term Memory Networks (BiLSTM), effectively extracting potential coupling information between loads. Literature (Wang et al, 2021) used a forecasting model combining MTL with Long Short-Term Memory Networks (LSTM) to forecast the trend curves of decomposed and reconstructed multi-energy loads. Additionally, it employed the Least Squares Support Vector Regression (LSSVR) method to forecast fluctuation curves.…”

Section: Introductionmentioning

confidence: 99%

Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Huang,

Wang,

Wang

et al. 2024

Front. Energy Res.

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To address the challenges posed by the randomness and volatility of multi-energy loads in integrated energy systems for ultra-short-term accurate load forecasting, this paper proposes an ultra-short-term multi-energy load forecasting method based on multi-dimensional coupling feature mining and multi-task learning. Firstly, a method for mining multi-dimensional coupling characteristics of multi-energy loads is proposed, integrating multiple correlation analysis methods. By constructing coupling features of multi-energy loads and using them as input features of the model, the complex coupling relationships between multi-energy loads are effectively quantified. Secondly, an ultra-short-term multi-energy load forecasting model based on multi-task learning and a temporal convolutional network is constructed. In the prediction model construction phase, the potential complex coupling characteristics between multiple loads can be fully explored, and the potential temporal associations and long-term dependencies within data can be extracted. Then, the multi-task learning loss function weight optimization method based on homoscedastic uncertainty is used to optimize the forecasting model, realizing automatic tuning of the loss function weight parameters and further improving the prediction performance of the model. Finally, an experimental analysis is conducted using the integrated energy system of Arizona State University in the United States as an example. The results show that the proposed forecasting method has higher prediction accuracy than other prediction methods.

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Section: Introductionmentioning

confidence: 99%

Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Huang,

Wang,

Wang

et al. 2024

Front. Energy Res.

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“…T HE accelerated construction of regional integrated energy system (RIES) improves the comprehensive utiliza-tion efficiency of various energy forms and contributes to environmental protection. The RIES utilizes the advanced technologies of physical information systems and novel management models to integrate heterogeneous energy sources such as electricity and heat within a volume of time and space [1], [2]. As a result, the high-quality operation of diversified energy can be systematically achieved.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

Ge,

Li,

Yan

et al. 2023

Journal of Modern Power Systems and Clean Energy

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To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system (RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction (M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search (CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.

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Urban monthly power load forecasting based on economy-meteorology-gas demand coupling

et al. 2022

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Multienergy Load Forecasting for Regional Integrated Energy Systems Considering Multienergy Coupling of Variation Characteristic Curves

Cited by 7 publications

References 30 publications

Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Ultra-short-term multi-energy load forecasting for integrated energy systems based on multi-dimensional coupling characteristic mining and multi-task learning

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

Urban monthly power load forecasting based on economy-meteorology-gas demand coupling

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