Optimizing HVAC Energy Usage in Industrial Processes by Scheduling Based on Weather Data

Abstract

Industrial buildings are demonstrating increasing rates of energy consumption, with heating, ventilation, and air conditioning (HVAC) typically constituting over 50% of this consumption. However, these energy requirements are heavily influenced by weather conditions based on the season, the time of day, and different in-building activities. These activities take place in industrial setup over 24 h and have different HVAC energy requirements. In this paper, we propose a binary (0,1) integer linear programming approach to efficiently schedule activities based on weather forecasting, thus minimizing the energy required by HVAC. Experimental results show that energy consumption can be reduced by up to 30%.