Day Ahead Scheduling to Optimize Industrial HVAC Energy Cost Based on Peak/OFF-Peak Tariff and Weather Forecasting

Abstract

The cost consumption of industrial buildings is increasing, with heating, ventilation and air conditioning (HVAC) functions typically comprising half of all cost requirements. These cost requirements are heavily influenced by weather conditions based on the season and time of day, which may require different amounts or types of HVAC to provide habitable working conditions. Similarly, different activities that take place in an industrial building during a 24-h period also have different HVAC requirements. In this paper, we propose an optimal scheduling strategy based on activity type and weather forecasting to conserve HVAC costs. We have formulated the activity scheduling problem as a binary integer linear programming problem (BILP), and solved it using a CPLEX solver. Experimental results show that by scheduling activities, using 8-h time slots, we can achieve a reduction in costs of up to 27%. In addition, with 1-h time slots, optimal activity scheduling can yield up to a 38% reduction in costs.