Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning

Zhichao Qiu, Ye Tian (), Yanhong Luo, Taiyu Gu and Hengyu Liu
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Zhichao Qiu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Ye Tian: Institute of Electric Power Science, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China
Yanhong Luo: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Taiyu Gu: Institute of Electric Power Science, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China
Hengyu Liu: Institute of Electric Power Science, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China

Sustainability, 2024, vol. 16, issue 23, 1-24

Abstract: Virtual power plants (VPPs) have emerged as an innovative solution for modern power systems, particularly for integrating renewable energy sources. This study proposes a novel prediction approach combining improved K-means clustering with Time Convolutional Networks (TCNs), a Bi-directional Gated Recurrent Unit (BiGRU), and an attention mechanism to enhance the forecasting accuracy of wind and photovoltaic power generation in VPPs. The proposed TCN-BiGRU-Attention model demonstrates superior predictive performance compared to traditional models, achieving high accuracy and robustness. These results provide a reliable basis for optimizing VPP operations and integrating renewable energy sources effectively.

Keywords: virtual power plant; cluster analysis; wind and photovoltaic power generation; TCN-BiGRU-Attention prediction model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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