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.
Highlights
The International Energy Agency (IEA) reported that the final energy use in the building sector grew 240 million tons of oil equivalent (Mtoe) from 2010 to 2018, while the share of fossil fuels only decreased slightly, from 38% in 2010 to 36% in 2018 [1]. This growth in energy consumption has a noticeable impact on the environment through the need to deplete more fossil fuels that increase greenhouse gases
The results results for implementing models to the simulated office (Figure building4)(Figure in for implementing differentdifferent models to the simulated office building in Doha4)are
It considers the weather information, building characteristics, and the electricity pricing profile to calculate the optimal cooling setpoint temperatures for all building zones. This model was compared with three other cooling control models, including the always-on thermostat, the schedule-based model, and the rule-based occupancy-driven model. These methods were implemented in a simulated office building, and the results showed considerable energy saving through cooling energy systems
Summary
The International Energy Agency (IEA) reported that the final energy use in the building sector grew 240 million tons of oil equivalent (Mtoe) from 2010 to 2018, while the share of fossil fuels only decreased slightly, from 38% in 2010 to 36% in 2018 [1]. This growth in energy consumption has a noticeable impact on the environment through the need to deplete more fossil fuels that increase greenhouse gases. The need to develop efficient and practical models to improve energy use in these buildings is paramount
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