Study on photovoltaic power prediction with TSO-LSTM-XGBoost coupled model accounting for weather factors

IF 3.1 4区工程技术 Q3 ENERGY & FUELS

International Journal of Green Energy Pub Date : 2024-08-29 DOI:10.1080/15435075.2024.2390159

Wenqi Ge, Xiaotong Wang, Yanbai Sun

引用次数: 0

Abstract

The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coup...

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利用考虑天气因素的 TSO-LSTM-XGBoost 耦合模型预测光伏发电量的研究

光伏发电本身的明确预测对电网的调度和运行具有重要意义。为了保证电力系统的稳定运行，本文提出了一种光伏发电的耦合预测方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Green Energy 工程技术-能源与燃料

CiteScore

6.60

自引率

9.10%

发文量

112

审稿时长

3.7 months

期刊介绍： International Journal of Green Energy shares multidisciplinary research results in the fields of energy research, energy conversion, energy management, and energy conservation, with a particular interest in advanced, environmentally friendly energy technologies. We publish research that focuses on the forms and utilizations of energy that have no, minimal, or reduced impact on environment, economy and society.