Open AccessResearch on EA-Xgboost Hybrid Model for Building Energy Prediction
Author(s) -
Yucong Wu,
Bo Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012082
Subject(s) - autoregressive integrated moving average , residual , energy consumption , computer science , hilbert–huang transform , energy (signal processing) , hyperparameter optimization , mode (computer interface) , renewable energy , model building , data mining , econometrics , artificial intelligence , statistics , time series , machine learning , engineering , mathematics , support vector machine , algorithm , physics , quantum mechanics , electrical engineering , operating system
Building energy forecast plays an important role in Intelligent Building. Due to its non-stationarity and uncertainty, the prediction accuracy of existing methods need to be further improved. In view of this problem, propose the EA-XGBoost model, which combines Empirical Mode Decomposition (EMD), ARIMA and XGBoost model to predict building energy consumption. First, EMD is used to decompose the consumption data into multiple Intrinsic Mode Functions(IMF). Afterwards, ARIMA model is applied for each IMF to get regression result, then sum the results and calculate the residual. Taking the residual as an input feature of XGBoost, combined with other energy-related factors such as dry and wet bulb temperature, using XGBoost after Grid-Search to predict building energy consumption data. Compared with ARIMA and XGBoost model, EA-XGBoost hybrid model performs best in forecasting building energy consumption dataset which provided by the US National Renewable Energy Laboratory. The experiment shows the feasibility and effectiveness of the new model.