Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

• Main Authors: Feng, Y. (Author), Huang, Y. (Author), Knefaty, A.D (Author), Lou, J. (Author), Shang, H. (Author), Yao, J. (Author), Zheng, R. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: 12 × 12 grid search; Accurate prediction; Air conditioning; Air conditioning energy consumption; Building energy consumption; Energy utilization; Forecasting; Gaussian distribution; Gaussian noise (electronic); Gaussian process regression; Grid search; HVAC system; In-buildings; Meteorology; Office buildings; prediction of air-conditioning energy consumption; Prediction of air-conditioning energy consumption; Regression analysis; Search engines; System energy consumption
• Online Access: View Fulltext in Publisher

 Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.