A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree

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. and loads, especially PV power, can be managed in a more active way. For example, power system dispatch centers can arrange dispatch plans more reasonably and make adjustments more timely [6]. Moreover, smart grids can control a variety of power and reduce the capacity and operating costs of energy storage [7,8].According to input variables, PV prediction methods are able to be divided into two classes, namely, direct prediction and indirect prediction. Direct prediction methods only need historical power data, which is based on time series characteristics. Auto-Regressive and Moving Average Model (ARMA) and Autoregressive Integrated Moving Average Model (ARIMA) are the typical time-series prediction methods. In contrast, indirect prediction methods involve wider input data such as solar radiation, temperature, and other meteorological information provided by numerical weather prediction (NWP) systems. PV power output is closely related to the meteorological factors, and thus indirect prediction methods are generally more accurate and widely used. According to the algorithms used, PV prediction methods can be divided into physical methods and statistical methods. Generally, physical methods firstly predict the factors that directly influence PV power output and then obtain the PV output power by using the forecast values of the factors as the input of the physical model. On the other hand, statistical methods use historical data to build a statistical model based on some machine le...

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