The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

Abstract

Cooling systems play a key role in maintaining human comfort inside buildings. The key challenges that are facing conventional cooling systems are the rapid growth of total cooling energy and annual electricity consumption in commercial buildings. This is even more significant in countries with an arid climate, where the cooling systems are typically working 80% of the year. Thus, there has been growing interest in developing smart control models to assign optimal cooling setpoints in recent years. In the present work, we propose an occupancy-based control model that is based on a non-linear optimization algorithm to efficiently reduce energy consumption and costs. The model utilizes a Monte-Carlo method to determine the approximate occupancy schedule for building thermal zones. We compare our proposed model to three different strategies, namely: always-on thermostat, schedule-based model, and a rule-based occupancy-driven model. Unlike these three rule-based algorithms, the proposed optimization approach is a white-box model that considers the thermodynamic relationships in the cooling system to find the optimal cooling setpoints. For comparison, different case studies in five cities around the world were investigated. Our findings illustrate that the proposed optimization algorithm is able to noticeably reduce the cooling energy consumption of the buildings. Significantly, in cities that were located in severe hot regions, such as Doha and Phoenix, cooling energy consumption can be reduced by 14.71% and 15.19%, respectively.

Design of an EnergyPlus Model-Based Smart Controller for Maintaining Thermal Comfortable Environment in Non-Domestic Building

“…Learning MPC is described as having high potential based on four different characteristics. An occupancy-based control model based on a nonlinear optimization scheme for reducing energy cost with a better comfort level is proposed in [21]. The Monte-Carlo simulation method is used to determine the probabilistic occupancy schedule.…”

Analysis of Human-Building Interactions in Office Environments: to What Extent Energy Saving Boundaries can be Displaced?

“…Since human and social dimensions are complex phenomenon, they have to be fully scrutinized for different regions and demographics. To the best of our knowledge, there is still inadequate literature to investigate a technical analysis of human-building interactions reflecting factors such as awareness, norms, prior habits, responsibility factors, demographic, and socioeconomic to measure the influence of occupancy drivers on the energy demand profile of commercial buildings and potential savings as a result of behavioral adjustments on saving energy (Nazemi et al, 2021). Besides, knowing the fact that experimental assessments and onsite measurements are prone to error and biased as well as flexibility issues with regard to sensitivity analysis of preferable end-uses and indoor environment adjustments, there is still a lack in terms of adequate simulation analysis on this matter.…”