Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A short-term photovoltaic power prediction model based on the gradient boost decision tree

Wang, Jidong, Li, Peng, Ran, Ran, Che, Yanbo and Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714 2018. A short-term photovoltaic power prediction model based on the gradient boost decision tree. Applied Sciences 8 (5) , p. 689. 10.3390/app8050689

Full text not available from this repository.

Abstract

Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These features may have a negative influence on power systems. As a result, accurate and timely power prediction data is necessary for power grids to absorb solar energy. In this paper, we propose a new PV power prediction model based on the Gradient Boost Decision Tree (GBDT), which ensembles several binary trees by the gradient boosting ensemble method. The Gradient Boost method builds a strong learner by combining weak learners through iterative methods and the Decision Tree is a basic classification and regression method. As an ensemble machine learning algorithm, the Gradient Boost Decision Tree algorithm can offer higher forecast accuracy than one single learning algorithm. So GBDT is of value in both theoretical research and actual practice in the field of photovoltaic power prediction. The prediction model based on GBDT uses historical weather data and PV power output data to iteratively train the model, which is used to predict the future PV power output based on weather forecast data. Simulation results show that the proposed model based on GBDT has advantages of strong model interpretation, high accuracy, and stable error performance, and thus is of great significance for supporting the secure, stable and economic operation of power systems

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: MDPI
ISSN: 2076-3417
Date of Acceptance: 26 April 2018
Last Modified: 25 Oct 2022 13:39
URI: https://orca.cardiff.ac.uk/id/eprint/120454

Citation Data

Cited 99 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item