A method for predicting the power generation of residential solar panels that is easy to popularize in the Guangdong-Hong Kong-Macao Greater Bay Area‐‐‐‐‐based on GA optimization algorithm

Accurate prediction of distributed solar power generation is a key factor for the cost-effective operation of district heating and cooling networks. Traditional forecasts require multiple variables such as weather data and complex mathematical models, which are not suitable for promotion in the Guangdong-Hong Kong-Macao Greater Bay Area where the weather is changeable. This paper proposes that this kind of problem adopts a simple SVR model, and at the same time, in order to improve the accuracy of the model, the GA algorithm is used to optimize. The power generation prediction method of residential solar panels in this paper is verified by public datasets. The innovations of this paper are: 1) The GA algorithm is still used to improve the prediction accuracy on the basis of the SVR model; 2) The K-fold cross-validation method is used in the improvement of the GA algorithm, which greatly accelerates the calculation of the prediction results.