engleski

Modeling of an Active Household under Different Tariffs

Human society increasingly values energy efficiency and savings in energy consumption. In line with this, smart homes are receiving an increased attention because of their potential for a more efficient utilization of electric devices by the means of demand response (DR) programs. This paper presents a mixed-integer linear programming model of a household that contains all relevant appliances with the DR potential. For controllable appliances, real load profiles logged in the Demand Response Laboratory at the University of Zagreb Faculty of Electrical Engineering and Computing were used, while the K- means technique was used to cluster the representative days. The developed model is used to analyze possible savings in electricity costs under different tariff designs.

demand response, smart homes, thermal energy storage, batteries, machine learning, K-means

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