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Occupancy-based HVAC control using deep learning algorithms for estimating online preconditioning time in residential buildings
Highlights Occupancy-based HVAC control using dynamic conditioning time estimation. Deep learning algorithms are employed to provide online estimations of heat-up time. Profitability of smart thermostats is studied via comprehensive financial analysis. Occupancy information might negatively affect peak energy demand. Conservative setback provides relatively poor financial gains.
Abstract This paper presents a rule-based (RB) heating, ventilation, and air-conditioning (HVAC) control system using a multi-layer perceptron network, a deep learning algorithm, for estimating dynamic preconditioning time in residential buildings. The proposed system takes advantage of occupancy, indoor temperature, and weather data to make control decisions in buildings. The system performance is evaluated in terms of financial, demand-side management, energy-efficiency, and occupants’ thermal comfort. The proposed approach considers the perfect occupancy prediction assumption to remove the impact of the uncertainty associated with occupancy prediction and to estimate an upper bound limit for the system performance. The system performance is compared with that of conventional rule-based control approaches to show its effectiveness. To select the optimal control system, the TOPSIS method, as a multi-criteria decision-making approach, is employed. The sensitivity of the proposed system to the temperature setback is also assessed by considering conservative, medium, and deep setback bounds. It is demonstrated that the proposed system outperforms other alternatives when the deep and medium bounds are utilized. This study reveals two limitations of occupancy-based control systems by investigating their performance from financial and peak-demand points of view. First, these systems can cause peak-demand issues and increase the on-peak energy consumption by up to 10%. Secondly, employing a conservative setback can significantly decrease the financial merits of the system, leading to a discounted payback period of 10.87 years for implementing smart thermostats.
Occupancy-based HVAC control using deep learning algorithms for estimating online preconditioning time in residential buildings
Highlights Occupancy-based HVAC control using dynamic conditioning time estimation. Deep learning algorithms are employed to provide online estimations of heat-up time. Profitability of smart thermostats is studied via comprehensive financial analysis. Occupancy information might negatively affect peak energy demand. Conservative setback provides relatively poor financial gains.
Abstract This paper presents a rule-based (RB) heating, ventilation, and air-conditioning (HVAC) control system using a multi-layer perceptron network, a deep learning algorithm, for estimating dynamic preconditioning time in residential buildings. The proposed system takes advantage of occupancy, indoor temperature, and weather data to make control decisions in buildings. The system performance is evaluated in terms of financial, demand-side management, energy-efficiency, and occupants’ thermal comfort. The proposed approach considers the perfect occupancy prediction assumption to remove the impact of the uncertainty associated with occupancy prediction and to estimate an upper bound limit for the system performance. The system performance is compared with that of conventional rule-based control approaches to show its effectiveness. To select the optimal control system, the TOPSIS method, as a multi-criteria decision-making approach, is employed. The sensitivity of the proposed system to the temperature setback is also assessed by considering conservative, medium, and deep setback bounds. It is demonstrated that the proposed system outperforms other alternatives when the deep and medium bounds are utilized. This study reveals two limitations of occupancy-based control systems by investigating their performance from financial and peak-demand points of view. First, these systems can cause peak-demand issues and increase the on-peak energy consumption by up to 10%. Secondly, employing a conservative setback can significantly decrease the financial merits of the system, leading to a discounted payback period of 10.87 years for implementing smart thermostats.
Occupancy-based HVAC control using deep learning algorithms for estimating online preconditioning time in residential buildings
Esrafilian-Najafabadi, Mohammad (Autor:in) / Haghighat, Fariborz (Autor:in)
Energy and Buildings ; 252
19.08.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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