A Prosumer Power Prediction Method Based on Dynamic Segmented Curve Matching and Trend Feature Perception

Biyun Chen (), Qi Xu, Zhuoli Zhao, Xiaoxuan Guo, Yongjun Zhang, Jingmin Chi and Canbing Li
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Biyun Chen: Key Laboratory of Power System Optimization and Energy Saving Technology, Guangxi University, Nanning 530004, China
Qi Xu: Key Laboratory of Power System Optimization and Energy Saving Technology, Guangxi University, Nanning 530004, China
Zhuoli Zhao: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Xiaoxuan Guo: Electric Power Research Institute, Guangxi Power Grid Corporation, Nanning 530023, China
Yongjun Zhang: School of Electric Power, South China University of Technology, Guangzhou 510640, China
Jingmin Chi: Guangxi Minhai Energy Co., Ltd., Nanning 530012, China
Canbing Li: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Sustainability, 2023, vol. 15, issue 4, 1-18

Abstract: With the massive installation of distributed renewable energy (DRE) generation, many prosumers with the dual attributes of load and power supply have emerged. Different DRE permeability and the corresponding peak-valley timing characteristics have an impact on the power features of prosumers, so new models and methods are needed to reflect the new features brought about by these factors. This paper proposes a method for predicting the power of prosumers. In this method, dynamic segmented curve matching is applied to reduce the complexity of source–load coupling features and improve the effectiveness of the input features, and trend feature perception based on a temporal convolutional network (TCN) was applied to grasp the power trend of prosumers by predicting the multisegment trend indexes. The LST-Atten prediction model based on a temporal attention mechanism (TAM) and a long short-term memory (LSTM) network was applied to predict “day-ahead” power, which combines the trend indexes and similar curve sets as the input. Simulation results show that the proposed model has higher accuracy than individual models. Furthermore, the proposed model can maintain prediction stability under different renewable energy permeability scenarios.

Keywords: dynamic segmented curve matching; LST-Atten; power prosumer; power prediction; trend feature perception (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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