Predictive smart thermostat controller for heating, ventilation, and air-conditioning systems

Soudari, Mallikarjun; Kaparin, Vadim; Srinivasan, Seshadhri; Seshadhri, Subathra; Kotta, Ülle

doi:10.3176/proc.2018.3.11

Cited by 10 publications

(2 citation statements)

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“…Compared to other control objects, the control of HVAC has unique challenges, including nonlinear dynamics, time-varying system dynamics and setpoints, time-varying disturbances, interacting, and at times con icting control loops [7]. To simplify the HVAC model, linearized sampled data were used with a regression analysis method, such as least absolute shrinkage and selection operator [8]. Economic objective functions are offered with predictive control of HVAC for load transfer and cost saving [9].…”

Section: Introductionmentioning

confidence: 99%

Development of a Coupled EnergyPlus-MATLAB Simulation Based on LSTM for Predictive Control of HVAC System

Cong

Hou

et al. 2022

Mathematical Problems in Engineering

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To avoid unnecessary energy waste due to the single temperature setpoints of the heating, ventilation, and air conditioning (HVAC) system during the seasonal variation period, this study proposed a long-short-term memory (LSTM) neural network to predict and control the temperature setpoint. The thermal comfort, cooling rate, and heating rate were predicted with outdoor environment parameters and occupant count. A large scale of operation data was collected from the EnergyPlus simulation, which was previously developed based on the characteristics of a real case study house. Different kinds of input characteristics were offered to test the stable use of LSTM and other artificial neural networks. This paper discusses the development of a Matlab EnergyPlus co-simulation to predict and control the temperature setpoints of a variable air volume system, especially the relationship between temperature setpoint and energy consumption. The simulation results indicate the advantages of the LSTM prediction of energy consumption and the potential for energy saving with predictive control.

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Section: Introductionmentioning

confidence: 99%

Development of a Coupled EnergyPlus-MATLAB Simulation Based on LSTM for Predictive Control of HVAC System

Cong

Hou

et al. 2022

Mathematical Problems in Engineering

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“…The causes of these gaps are diverse, including behavioral habits of occupants and construction flaws [41]. The evaluation of the actual thermal properties of the building stock from monitored data is widely considered advantageous compared to tabulated data to improve the overall quality of the building process by feeding back the measured data [42].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Water Flow Glazing: Transient Response in Real Test Rooms

et al. 2020

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The extensive use of glass in modern architecture has increased the heating and cooling loads in buildings. Recent studies have presented water flow glazing (WFG) envelopes as an alternative building energy management system to reduce energy consumption and improve thermal comfort in buildings. Currently, commercial software for thermal simulation does not include WFG as a façade material. This article aims to validate a new building simulation tool developed by the authors. Simulation results were compared with real data from a scale prototype composed of two twin cabins with different glazing envelopes: a Reference double glazing with solar-control coating and a triple water flow glazing. The results showed a good agreement between the simulation and the real data from the prototype. The mean percentage error of the indoor temperature cabin was lower than 5.5% and 3.2% in the WFG cabin and in the Reference glazing one, respectively. The indoor air temperature of the WFG cabin was 5 °C lower than the Reference one in a free-floating temperature regime when the outdoor air temperature was 35 °C and the maximum value of solar radiation was above 700 W/m2. WFG has energy-saving potential and is worthy of further research into the standardization of its manufacturing process and its ability to increase building occupants’ comfort.

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Adaptive Disturbance Observers for Building Thermal Model

Soudari

Srinivasan

Subathra

2020

Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications

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Predictive smart thermostat controller for heating, ventilation, and air-conditioning systems

Cited by 10 publications

References 0 publications

Development of a Coupled EnergyPlus-MATLAB Simulation Based on LSTM for Predictive Control of HVAC System

Development of a Coupled EnergyPlus-MATLAB Simulation Based on LSTM for Predictive Control of HVAC System

Experimental Validation of Water Flow Glazing: Transient Response in Real Test Rooms

Adaptive Disturbance Observers for Building Thermal Model

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