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IoT Reference Architectures
Published in Stavros Shiaeles, Nicholas Kolokotronis, Internet of Things, Threats, Landscape, and Countermeasures, 2021
V. Kelli, E. G. Sfakianakis, B. Ghita, P. Sarigiannidis
Devices that compose the smart home environment could be smart security systems, smart thermostats, smart fridges, smart lighting systems, windows, etc. Smart lighting systems can be used in order to detect the presence of people in the room and therefore be activated or deactivated accordingly. Smart thermostats are currently controlled remotely by the user; in the near future, they could be activated based on the smart home’s occupant’s schedule, in order to provide heat at a minimum level if there is no one at home, set the heat according to environmental variables or sense the user’s activity and therefore adjust heating to an optimal temperature. Thus, such devices, apart from contributing to living an effortless life, can also be applied for monitoring energy consumption and for energy-saving purposes for the smart household.
Smart Building Energy Systems
Published in Moncef Krarti, Energy Audit of Building Systems, 2020
As alternatives to programmable thermostats, smart thermostats are now available to control more effectively indoor temperature settings with no or little occupant interventions. These advanced thermostats include connected and smart thermostats. Connected thermostats are generally internet-connected devices that allow users to track and control remotely indoor temperatures. Smart thermostats are connected thermostats that employ advanced algorithms and/or sensors to control automatically indoor temperature settings in order to improve thermal comfort and/or EE. Specifically, smart thermostats integrate three technologies to adjust temperature settings based on home occupancy patterns and behaviors (Krarti, 2020): On-board occupancy sensors to detect movement within the rooms where the thermostats are locatedRemote occupancy sensors that are connected and linked to the thermostatsGeofencing (sometimes referred to as proximity sensing) that rely on linkage of mobile devices of all occupants to the thermostat to identify their physical location and proximity to a specific room and/or house.
IoT Smart Homes and Security Issues: An Overview
Published in Fadi Al-Turjman, Security in IoT-Enabled Spaces, 2019
Fadi Al-Turjman, Chadi Altrjman
Smart thermostat -This device allows the user to remotely control the heating and cooling system within the house. Furthermore, it can control the humidity and adjust the temperature of the house based on the user’s behavior inside the house. Moreover, these sensors can activate the energy saving mode when there are no users in the house. Applying cognitive technology to these sensors is a paving way for a smart home to be able to learn more about the residents and their temperature preferences.
Multivariate fault detection for residential HVAC systems using cloud-based thermostat data, part I: Methodology
Published in Science and Technology for the Built Environment, 2022
Fangzhou Guo, Austin P. Rogers, Bryan P. Rasmussen
An alternative data source for analysis and diagnostics in residential HVAC systems is smart thermostat data, including records of important, but limited, information including indoor/outdoor temperature, temperature setpoint, and heating/cooling operational mode. In recent years, smart thermostats have been widely adopted by homeowners, because they offer remote control and monitoring options (e.g., via smartphones), include learning algorithms to improve occupant comfort, and provide feedbacks to users regarding energy use. Meanwhile, the huge amount of data recorded by smart thermostats provides distinctive opportunities for FDD approaches, or even promotes a potential paradigm shift. Van der Ham et al. (2016) present two physics-based models and show that the thermostat data can detect building construction faults such as poor thermal insulation. Similarly, Rogers et al. (2013) focus on optimizing occupant behavior and develop an algorithm to provide personalized home heating advice to households. While these studies use experimental data, simulated thermostat data is also used for model validation. For example, Turner, Staino, and Basu (2017) propose a recursive least-squares model and identify airflow restrictions and total system failures.
Analysis of zone-by-zone indoor environmental conditions and electricity savings from the use of a smart thermostat: A residential case study
Published in Science and Technology for the Built Environment, 2020
Sukjoon Oh, Jeff S. Haberl, Juan-Carlos Baltazar
Lu et al. (2010) estimated average energy savings of 28% from a smart thermostat for a residential HVAC system using an EnergyPlus simulation model with measured and surveyed occupancy and sleep patterns. This study used a Hidden Markov Model to better control the HVAC system using the away, active, and sleep patterns of occupants. This study showed that a smart thermostat could provide larger energy savings in warmer climates because the cooling and heating setback schedules of a smart thermostat are more advantageous for the peak cooling loads during a mid-day and for the less heating loads in winter, respectively. Erickson, Carreira-Perpinan, and Cerpa (2011) also used EnergyPlus simulations to estimate HVAC energy savings of average 42% using a Markov Chain occupancy model based on measured occupancy data. They studied the statistical model for different occupancy schedules. They also accounted for the impact of ventilation levels from the estimated savings. Nest Labs (2012) used a calibrated building energy simulation model that showed the highest energy saving of 36% from the change of thermostat setpoints and from the absence of occupants based on varying scenarios. The scenarios covered two different occupant types for several setpoint setback schedules by considering nine U.S. climate zones. However, all the three papers used simulations and did not show zone-by-zone analysis from the achieved savings of a smart thermostat.
Evolving interaction: a qualitative investigation of user mental models for smart thermostat users
Published in Architectural Science Review, 2023
Ruth Tamas, William O’Brien, Mario Santana Quintero
Smart thermostats present new features, many with the goal of promoting energy-savings. However, their influence remains somewhat undetermined. All participants in this study received feedback related to energy use (e.g. heating system run times) and could compare their profile with other smart thermostat users. However, most (9/10) users noted this did not influence their consumption but was ‘good to know’ or ‘nice to see’. While these features received positive reception, their intended impact may not be fully realized.