The application of various types of renewable energy on the building side is becoming more and more popular. Forecasting of building electricity consumption plays an increasingly important role in the balance of energy supply and demand

stable grid operation

and peak demand response. Although many data-driven models have been widely used in energy consumption prediction

there is still a lack of short-term prediction models with high prediction accuracy and strong generalization ability. In order to solve this problem

a classification and integration energy consumption prediction method based on the characteristics of building energy consumption and combined with data mining technology is proposed. Firstly

the recursive feature elimination method is used to screen the features of the data

and the fuzzy C-means clustering algorithmisused to cluster the training set data

meanwhile

K-nearest neighbor methodis used to classify the validation set and test set data. Then

five hybrid data-driven models combined with intelligent optimization algorithms areselected as sublearners

and each type of data is predicted respectively. Finally

multiple linear regression method is used to integrate the results. The accuracy of the ensemble prediction model is better than that of single sub-model

and it has potential to predict different building types and energy use scales.