Short-Term User Load Forecasting Based on Deep Random Forest: Take Jinhua City as an Example

By crawling weather data and combining with user load data in Jinhua City, a deep random forest algorithm is introduced to implement short-term user load forecasting. With four evaluation indicators, by comparing the support vector regression algorithm, the K-nearest neighbor algorithm, the Bayesian ridge regression algorithm, the random forest algorithm, and several neural network algorithms, it is found that the deep random forest algorithm has the best performance, and followed by the support vector regression. However, the neural network algorithm performed mediocre on this dataset.