Abstract:
Buildings account for a signi cant proportion of overall energy consumption across the world.
Heating Ventilation and Air Conditioning (HVAC) typically consumes a major proportion (e.g.
32% in India) of the total building energy consumption. While centralized HVAC systems
are more prevalent in developed countries, separate room level Air Conditioners (ACs) are a
commonplace in developing countries, such as India. Poor building insulation in developing
countries, together with an option to easily control room level air conditioning, presents a
major opportunity for energy conservation in these countries. We propose PACMAN - a novel
approach for predicting the energy consumption of room level AC. PACMAN involves learning
a thermal model of the room from historical usage and combines this model with the weather
forecast for user's location to guide the user towards optimized AC settings in order to balance
user comfort and energy e ciency. Empirical validation was performed using a real world
study, conducted across 7 homes in India, with collective data for a duration of 2200 hours in
total. PACMAN achieved more than 90% accuracy in predicting the energy consumption across
di erent ACs, room types and set temperatures used during the data collection. We further
describe a prototype realization of the proposed PACMAN system towards achieving reduced
AC energy consumption with better feedback and control.
