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MILP-based optimal day-ahead scheduling for system-centric CEMS supporting different types of homes and energy trading
Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118. 2 s for a 500-home community, which is a short time for day-ahead scheduling of a community.
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization
The demand response (DR) program of a traditional HEMS usually intervenes appliances by controlling or scheduling them to achieve multiple objectives such as minimizing energy cost and maximizing user comfort.
MILP-based Imitation Learning for HVAC control
Hence, in this study, we propose MILP-based imitation learning method to control a HVAC system without using the forecast information in order to reduce energy cost and maintain thermal comfort at a given level.
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