# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 1 Introduction  
With the development of the photovoltaic (PV) power generation industry and promotion of relevant technologies, the price of PV systems is now much lower than in past years [1]. The conversion efficiency of solar energy has also been improved due to the development of maximum power point tracking technology [2]. The development of the power electronics industry makes it possible to produce grid connected inverters with good performance and low cost, which makes it easier to attach solar energy to the power grid [3]. In addition, faced with severe environmental problems, governments over the world have also issued various policies to support the development of renewable energy sources [4]. Therefore, in recent years, the installed capacity of grid-connected PV systems has continuously increased and the construction and maintenance cost have been declining. The PV power generation industry has been developed at high speed as well as with good quality. However, due to the fact that PV power output is restricted by the natural environment and is thus intermittent, large-scale grid-connected PV systems may result in grid voltage fluctuations, which bring great challenges to power system operation and regulation [5]. With the emergence of new technologies such as smart grid, active distribution network, micro grid, etc., power grids are able to get more accurate and timely PV power forecast information, based on which various energiesand 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 learning algorithms, and then predict the PV power output directly without building a specific physical model. Along with the rapid update of computer hardware and development of data mining and machine learning, increasing intelligent algorithms have been used in the field of PV power forecast [9]. ANN is the mostly used method in PV power forecast [10]. Compare the artificial neural network (ANN) with the linear regression method in PV power forecast. Some modified ANN models have also been proposed. For example, wavelet decomposition was used to improve ANN in [11]. The back-propagation neural network (BPNN) based model was used in [12], and the parameters of BPNN were optimized in [13]. ANN can be used to solve most kinds of forecast problems and is able to be quickly adjusted to any practical models.