# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 1 Introduction
However, there are still some defects: (a) ANN needs a large amount of data for training and the training time increases significantly with the increasing complexity of neural networks; (b) the internal mechanism is unable to be understood due to lack of interpretability; (c) the reliability of the ANN model depends largely on topological structure and parameter selection. In order to overcome these limitations, some algorithms based on statistical theory were proposed. The support vector machine (SVM) is a typical example. A prediction model based on weather classification and SVM was proposed in [14]. In [15], researchers modified SVM with the genetic algorithm, to avoid local optimums during the parameter selection process. In [16], researchers firstly used Principal Component Analysis (PCA) to reduce the dimension of input data, and then trained the SVM to reduce training time. In [17], researchers used self-organizing map (SOM) to cluster weather types before training the SVM. In [18], the researchers compared most machine learning methods and obtained a recapitulative conclusion. Many methods including ANN and SVM can get better results than classical regression. Also, ANN and SVM give a similar forecast accuracy in photovoltaic power prediction. But compared to ANN, SVM needs a much lower amount of input data and maps data to high dimension space to deal with nonlinear problems; the SVM optimization step is automatic while the structure of the ANN method is complicated. Thus SVM is more easy to use than ANN. Besides the advantages, SVM models are also hard to explain and train, and the SVM model has difficulty coping with a large-scale training sample. In recent years, 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. The ensemble machine learning algorithms train multiple base learners as ensemble members and get a single output combining their predictions [10]. Reference [19] proposed an integrated algorithm for averaging multiple ANN model results, and its prediction accuracy was higher than that of any single ANN model. 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. The simulation results showed that the ensemble models offered more accurate results than any individual machine learning model alone. In this paper, Gradient Boost Decision Tree (GBDT) algorithm is proposed to predict the power output for a PV power plant. The Gradient Boost Decision Tree (GBDT) takes shape by integrating the Gradient Boosting algorithm with the Decision Tree algorithm. The GBDT uses decision trees as weak learners and builds the model in a stage-wise manner by optimizing the loss function. Boosting method is a type of main method in the ensemble learning method and jointly builds a strong learner by combining weak learners through iterative methods. While not the same as other Boosting methods, the Gradient Boosting algorithm finishes the learning process by updating the loss function and gradient. The Decision Tree algorithm is a basic classification and regression method and it has a tree structure where each internal node represents a test on an attribute, each leaf node represents a category, and each branch represents a test output. In a word, as an ensemble machine learning algorithm, the GBDT is better than ANN and SVM in forecast accuracy. In this paper, we analyze the physical model of PV power generation and select the data that affects the PV output as the input of this model. Through the prediction of the short-term PV power generation for 15 min, the simulation results show that the prediction model based on the Gradient Boost Decision Tree algorithm has high accuracy under various light intensity conditions, compared with SVM and ARMA. The paper is organized as follows.