Work

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

Year: 2024

Type: article

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.

Source: Sustainability

DOI: 10.3390/su162310740

Authors: Zhichao Qiu, Ye Tian, Yanhong Luo, Taiyu Gu, Hengyu Liu

Institutions: Northeastern University, State Grid Liaoning Electric Power Company (China)

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728