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.
Highlights
With the development of the photovoltaic (PV) power generation industry and promotion of relevant technologies, the price of PV systems is much lower than in past years [1]
The prediction model based on Gradient Boost Decision Tree (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
The solar power plant in Ashland is located at 42.19° N and 122.70° W, averaging over 595 m prediction model based on the GBDT algorithm
Summary
With the development of the photovoltaic (PV) power generation industry and promotion of relevant technologies, the price of PV systems is much lower than in past years [1]. Statistical methods use historical data to build a statistical model based on some machine learning algorithms, and predict the PV power output directly without building a specific physical model. Many ensemble machine learning algorithms, which combine multiple models in a reasonable way, were applied to the field of PV power forecast, which usually gave a better performance than one single algorithm. Reference [20] proposed three different methods for ensemble probabilistic forecasting, which were derived from seven individual machine learning models, to generate 24 h ahead solar power forecast. Gradient Boost Decision Tree (GBDT) algorithm is proposed to predict the power output for a PV power plant. As an ensemble machine learning algorithm, the GBDT is better than ANN and SVM in forecast accuracy.
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