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- W4363649641 abstract "The influencing factors of public building energy consumption are complex, and accurate prediction of public building energy consumption is of great significance for energy conservation. Studying new energy consumption prediction methods can simplify the prediction process and improve the prediction accuracy. Taking the energy consumption data of a large public building as the sample, the principal component analysis is carried out to remove the information redundancy and eliminate the correlation between input quantities; Based on the analysis of random forest and AdaBoost algorithm, the RF AdaBoost prediction model is constructed. The results show that the prediction model has better prediction effect, which can be used to predict and supervise building energy consumption, improve energy consumption facilities, and achieve effective energy utilization." @default.
- W4363649641 created "2023-04-11" @default.
- W4363649641 creator A5005542093 @default.
- W4363649641 date "2023-02-24" @default.
- W4363649641 modified "2023-10-18" @default.
- W4363649641 title "Energy consumption prediction of public buildings based on PCA-RF-AdaBoost" @default.
- W4363649641 doi "https://doi.org/10.1109/eebda56825.2023.10090762" @default.
- W4363649641 hasPublicationYear "2023" @default.
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