Geodesy

Solar irradiance

Quantum mechanics

Benchmark (surveying)

Engineering

Irradiance

Statistics

Probabilistic forecasting

Support vector machine

Probabilistic logic

Data mining

Computer science

Solar energy

Physics

Renewable energy

Mathematics

Estimator

Geography

Kernel density estimation

Artificial intelligence

Meteorology

Machine learning

Electrical engineering

Preprocessor

Energy Reports

Solar energy has received increasing attention as renewable clean energy in recent years. Power grid operators and researchers widely value probabilistic solar irradiance forecasting because it can provide uncertainty measurement for future PV production. This paper proposes a probabilistic prediction model of solar irradiance based on XGBoost. Specifically, after data preprocessing, historical data is utilized for training a point prediction model based on XGBoost. Since XGBoost is obtained by minimizing the residuals of successive iterations of multiple trees, when predicting solar irradiance at a certain time in the future, these trees can generate multiple predicted values of irradiance iteratively. Finally, the kernel density estimation method is applied to transform the above prediction results in probability prediction intervals under different confidence levels. Experimental results on public data sets show that this method has better accuracy than other benchmark algorithms. The experiment also shows that the method proposed in this paper requires less training time and simple parameter adjustment, which is very suitable for application in engineering practice.

Probabilistic solar irradiance forecasting based on XGBoost