基于核密度估计和CatBoost算法的光伏功率预测方法

针对传统单一模型难以有效分析历史数据的波动性规律,从而导致光伏功率预测精度不高的问题,提出了一种基于核密度估计和CatBoost算法的超短期光伏功率预测方法。首先,采集相关的辐射、温度和湿度等特征量,创建光伏功率概率分布统计模型;其次,基于功率分布特性和CatBoost算法构建光伏电站功率预测模型;最后,将所提出的模型应用到实际算例中验证其有效性。通过与常用的预测算法对比,所提模型的预测误差相较于传统模型SVR、DTR、KNN、LSTM、LightGBM分别下降了27.59%、8.69%、16.21%、23.33%和12.56%。

A Photovoltaic Power Prediction Method Based on Kernel Density Estimation and CatBoost Method

In order to address the problem of low accuracy in photovoltaic power forecasting caused by the difficulty of traditional single models in effectively analyzing the volatility patterns in historical data, this paper introduces a combination of the categorical boosting algorithm, kernel density estimation, and ultra-short-term photovoltaic power forecasting.Firstly, feature engineering is applied to extract the characteristic vectors of radiation, temperature and humidity related to the modeling, and the statistical model of photovoltaic power probability distribution is created.Secondly, based on the power distribution characteristics and CatBoost algorithm, the power prediction model of PV station is proposed.Finally, the mentioned model algorithm is applied to the actual site calculation to verify its effectiveness, and the comparison with the existing common prediction algorithms shows that the model proposed in this paper can effectively improve the prediction performance.Compared with the traditional models SVR, DTR, KNN, LSTM and Light GBM, the prediction error of SRMSE is decreased by 27.59%, 8.69%, 16.21%, 23.33% and 12.56%, respectively.