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Optimizing HVAC Systems in Buildings with Machine Learning Prediction Models: an Algorithm Based Economic Analysis

The Heating, Ventilation and Air Conditioning (HVAC) systems have been widely employed in office buildings in China. However, these 24-hour running machines can result in plenty of unnecessary costs due to abundancies of cooling power created by extra power used. To cope with this scenario and maximize HVAC’s running efficiency, this study builds prediction models with machine learning (ML) algorithms (including Gradient Boosting, Adaboost, and Random Forest) to optimize the existing systems with data derived from an office building in China. The goal of optimization is to attain the same of amount of cooling while less power is consumed, which is done by controlling the temperature setpoints to a more reasonable setting. With trained models, the results of optimization suggest a promising outcome that, for a single office building, at least ¥30156.40 could be saved annually, yielded by economic tools like Net Present Value (NPV), Life Cycle Cost Analysis (LCCA), and Social Carbon Cost (SCC). Findings in this paper could be generalized to other instances in the field of energy: power distribution grid, home appliances, irrigation systems etc. Apart from suggesting an optimization methodology for HVAC systems, this paper also aims to inspire more future work in energy conservation with more advanced technologies like machine learning, in the hope that the volatile, deteriorating global climate could be mitigated by cleaner and greener approaches in everyday life.