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Energy saving and carbon emission reduction in buildings is one of the keymeasures in combating climate change. Heating, Ventilation, and AirConditioning (HVAC) system account for the majority of the energy consumptionin the built environment, and among which, the chiller plant constitutes thetop portion. The optimization of chiller system power consumption had beenextensively studied in the mechanical engineering and building service domains.Many works employ physical models from the domain knowledge. With the advanceof big data and AI, the adoption of machine learning into the optimizationproblems becomes popular. Although many research works and projects turn tothis direction for energy saving, the application into the optimization problemremains a challenging task. This work is targeted to outline a framework forsuch problems on how the energy saving should be benchmarked, if holistic orindividually modeling should be used, how the optimization is to be conducted,why data pattern augmentation at the initial deployment is a must, why thegradually increasing changes strategy must be used. Results of analysis onhistorical data and empirical experiment on live data are presented.

AI Chiller: An Open IoT Cloud Based Machine Learning Framework for the  Energy Saving of Building HVAC System via Big Data Analytics on the Fusion of  BMS and Environmental Data