Regional wind-photovoltaic combined power generation forecasting based on a novel multi-task learning framework and TPA-LSTM

光伏系统 计算机科学 风力发电 发电 可再生能源 任务(项目管理) 人工智能 功率(物理) 机器学习 工程类 系统工程 电气工程 物理 量子力学
作者
Yuejiang Chen,Jiang‐Wen Xiao,Yan‐Wu Wang,Yuanzheng Li
出处
期刊:Energy Conversion and Management [Elsevier BV]
卷期号:297: 117715-117715 被引量:30
标识
DOI:10.1016/j.enconman.2023.117715
摘要

Existing renewable power generation forecasting methods mainly focus on a single energy source and fail to effectively capture the spatio-temporal correlation between different power generation resources. Furthermore, the current single-site power forecasting no longer fulfills the demands of grid dispatch. This paper introduces an innovative framework for multi-task learning and uses it to achieve regional wind-photovoltaic combined power generation forecasting. First, this paper employs Maximum Information Coefficient (MIC) to identify the crucial meteorological features affecting power generation and analyze the complementarity and correlation between wind and photovoltaic power generation. Then, an innovative multi-task learning framework is proposed that separates task-specific components and shared components, allowing each task to select adaptive information that benefits itself. Besides, this paper proposes a loss optimization strategy to balance the loss magnitude and training velocity of different tasks. In order to effectively share the coupling information among the two kinds of power generation, the proposed framework is adopted to construct the regional wind-photovoltaic combined power generation forecasting model based on Temporal Pattern Attention LSTM (TPA-LSTM) algorithm. Finally, the efficiency and superiority of the proposed method are validated through several verification and comparison case studies.
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