While previous studies have attempted to understand and predict users' behaviors and preferences for residential thermostats, they have been restricted by a lack of available data. Because of ...practical constraints, researchers previously relied on short observation periods, small sample groups, and/or participants close in physical location. The advent of the connected thermostat and its inherent centralized data collection now allows for such studies to be performed without the onus of data collection. Specifically, in this article we focus on the ‘Donate Your Data’ dataset made available by the thermostat manufacturer ecobee Inc. The dataset, consisting of more than 10,000 connected thermostats installed across North America and spanning multiple years, was used to investigate how users' comfort decisions are affected by exterior stimuli such as climate regions, seasonal patterns, and utility rates. Our analysis indicates that seasonality and climate region affected user preferences while utility rates did not contribute to meaningful variation in behavior. Further investigation explored if behavioral user types could be identified based on variation in occupied and unoccupied setpoints, thermostat overrides with holds, or heating and cooling setpoint selection. We did not find distinct user clusters to be identifiable based on any of the metrics; rather, occupant behavior in the population appeared to span more of a continuum across each metric.
•Utilized a new open dataset retrieved from over 10,000 connected thermostat devices.•Performed an exploratory analysis and characterization of the dataset.•Investigated the relation of climate, season, and price on the thermal preferences.•Attempted to categorize users based on their thermostat operation behaviors.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Most existing residential buildings adopt one single-zone thermostat to control the heating of rooms with different thermal conditions. This solution often provides poor thermal comfort and ...inefficient use of energy. The current market proposes smart thermostats and thermostatic radiator valves (TRVs) as cheap and relatively easy-to-install retrofit solutions. These systems provide increased freedom of installation, due to the use of wireless communication; however, the uncertainty of the measured air temperature, considering the thermostat placement, could impact the final heating performance. This paper presents a sensing optimization approach for a home thermostat, in order to determine the optimal retrofit configuration to reduce the sensing uncertainty, thus achieving the required comfort level and minimizing the retrofit’s payback period. The methodology was applied to a real case study—a dwelling located in Italy. The measured data and a simulation model were used to create different retrofit scenarios. Among these, the optimal scenario was achieved through thermostat repositioning and a setpoint of 21 °C, without the use of TRVs. Such optimization provided an improvement of control performance due to sensor location, with consequent energy savings of 7% (compared to the baseline). The resulting payback period ranged from two and a half years to less than a year, depending on impact of the embedded smart thermostat algorithms.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This paper presents an analysis conducted upon custom temperature setpoint change request data collected from four large office buildings. In three of these buildings, the setpoint changes were ...executed upon hot and cold complaints; and in the fourth building, the occupants could adjust their setpoints through their thermostats. Using concurrent indoor and outdoor temperature records, multivariate logistic regression models predicting the frequency of custom setpoint change requests were developed. The indoor temperatures that minimize the frequency of thermal complaints or thermostat overrides were determined with these models. It is argued that these temperatures can be used to select building specific default seasonal temperature setpoints. The results indicate that the majority of the setpoint change requests were either to increase the default 22 °C temperature setpoints during the cooling season or to decrease them during the heating season. Custom setpoint change requests were registered 24 times more frequently when occupants were permitted to make temporary thermostat overrides. It was also identified that the operators tend to make greater setpoint changes upon hot and cold complaints than the occupants make through their thermostat overrides.
•Custom temperature setpoint change requests from four office buildings were analyzed.•Models were developed to predict the frequency of setpoint change requests.•Models were used to determine building specific default seasonal temperature setpoints.•Majority of thermal complaints were to increase the default 22 °C setpoint in the summer.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This paper proposes bidding strategies and compensation mechanisms for a load aggregator (LA) that implements the direct thermostat control (DTC) program. By joining the DTC program, residential ...consumers allow their house temperature to vary within pre-specified zones at pre-specified hours and be controlled by the LA. They also receive compensation from the LA. The heating/cooling (H/C) load control model is integrated into a two-stage bidding framework. Demand-price bidding curves are derived for the day-ahead market to address price uncertainties. Demand-only bids are derived for the real-time (RT) market. The flexibility of H/C loads is exploited in RT to address uncertainties in thermal-related house characteristics as well as uncertainties in weather and non-H/C load. Two compensation policies are proposed and compared. A case study verifies the proposed method.
Demand response and dynamic retail pricing of electricity are key factors in a smart grid to reduce peak loads and to increase the efficiency of the power grid. Air-conditioning and heating loads in ...residential buildings are major contributors to total electricity consumption. In hot climates, such as Austin, Texas, the electricity cooling load of buildings results in critical peak load during the on-peak period. Demand response (DR) is valuable to reduce both electricity loads and energy costs for end users in a residential building. This paper focuses on developing a control strategy for the HVACs to respond to real-time prices for peak load reduction. A proposed dynamic demand response controller (DDRC) changes the set-point temperature to control HVAC loads depending on electricity retail price published each 15 minutes and partially shifts some of this load away from the peak. The advantages of the proposed control strategy are that DDRC has a detailed scheduling function and compares the real-time retail price of electricity with a threshold price that customers set by their preference in order to control HVAC loads considering energy cost. In addition, a detailed single family house model is developed using OpenStudio and Energyplus considering the geometry of a residential building and geographical environment. This HVAC modeling provides simulation of a house. Comfort level is, moreover, reflected into the DDRC to minimize discomfort when DDRC changes the set-point temperature. Our proposed DDRC is implemented in MATLAB/SIMULINK and connected to the EnergyPlus model via building controls virtual test bed (BCVTB). The real-time retail price is based on the real-time wholesale price in the ERCOT market in Texas. The study shows that DDRC applied in residential HVAC systems could significantly reduce peak loads and electricity bills with a modest variation in thermal comfort.
The advent of smart thermostats with real-time sensing raises the question of how to preemptively control heating, ventilation, and air conditioning (HVAC) systems to minimize energy usage while ...maintaining occupant comfort. To this end, we empirically compare a standard reactive deadband control to two new smart thermostat HVAC control methods: (1) a model-free reinforcement learning (RL) approach and (2) a novel model predictive control (MPC) method, whose solution is optimal with respect to its data-driven linear model. We evaluated the controls with 500 unique energy models of houses located in the United States. The models were modified to facilitate the short-term performance simulation required for residential HVAC systems. Overall, we found the MPC controller offers three distinct advantages over the RL and deadband methods: (1) MPC had the lowest average cost (defined as a custom weighted combination of runtime and comfort) of the evaluated controllers; (2) the MPC control’s linear model was able to reliably extrapolate from the sparse sample of training observations, thus enabling it to adapt quickly to recent data; and (3) in contrast to RL methods, MPC did not subject the houses or occupants to the discomfort of system exploration.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Understanding the determinants for the adoption of novel green consumer technologies is important to effectively foster their diffusion. Energy and environmental science literature often takes an ...approach based on economic variables such as objectively measureable household and technology characteristics. Increasingly, also subjective variables based on personal belief are considered. On the basis of a survey about the intention to adopt an exemplary novel green consumer technology (intelligent thermostats), we contribute to the clarification of the explanatory power of these two approaches. We first compare the economic model to the belief-based model and second, investigate how beliefs about the green technology are influenced by personal environmental norms and innovativeness. Our evaluation shows that the belief-based model explains considerably higher variance in the intention to adopt. Thereby the perceived hedonic satisfaction, usefulness, habit and facilitating conditions reveal as key determinants. Moreover, environmental norms show lower impact than personal innovativeness. In the discussion we consolidate these findings and point to the risk of omitted variable bias when selectively including belief-based variables in adoption models. Our findings suggest that policies can effectively accelerate the early market diffusion of green consumer technologies by incentivizing retailers to introduce and market such technologies.
•Adoption of a green consumer technology (energy-saving thermostats) is evaluated.•Subjective beliefs about the technology show higher impact than objective measures.•Key beliefs relate to pleasure, usefulness, habits and facilitating conditions.•Personal innovativeness is more relevant for adoption than environmental norms.•Isolated use of belief-based adoption determinants can lead to omitted variable bias.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
New concepts and strategies are continually being investigated to meet fuel efficiency targets for new generations of internal combustion engines. Thermal management methods can reduce fuel ...consumption by up to 4%. One approach for thermal management is a map-controlled thermostat. In the current work, this concept is investigated through a benchmarking approach and a methodology for deriving the response time curve for this type of thermostat. To better explore the benchmarking idea, characteristics of map-controlled thermostats for two different engines are identified, analyzed, and compared. Finally, the effects of two separate map-controlled thermostats on the average fuel consumption of engines are investigated for a driving cycle using one-dimensional simulation. It is shown that using a map-controlled thermostat can reduce average vehicle fuel consumption by about 1.7% relative to current vehicles with ordinary wax-type thermostats.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Since most people do not have the expertise to control ...the HVAC system efficiently, unnecessary energy consumption is often caused by wasteful behaviors (overheating/overcooling and operation without occupancy). New Internet of Things (IoT) products for a smart home have the potential for energy saving by reducing the unnecessary operation of the energy system in a residential house. However, many people still hesitate to adopt this product with uncertain economic benefit. This work explores a co-simulation platform to assess energy saving impact and economic benefits of occupancy driven thermostat in a residential house. An occupancy simulator was devised and utilized to consider the random nature of the occupancy in a typical single family residential house. Six HVAC system control strategies were explored based on three types of thermostats (always on; schedule based; and occupancy driven) as well as two setpoint control algorithms (fixed setpoint; and adaptive control). Energy-plus was integrated into the co-simulation platform, which evaluated energy consumption and indoor temperature of a residential house in five climate conditions. User's comfort level was evaluated using the adaptive model for the six different control strategies in each location. The result showed that the occupancy information-based control algorithm can save about between 11% and 34% of energy without significantly risking the occupant's comfort level. The work also suggests that the adaptive control model has a more tangible saving impact as IoT products can easily integrate outside temperature. Depending on the locations, adaptive control can save up to 54% of energy consumption, and the occupancy information can add additional energy saving impact by 20%. Payback period varies for different control strategies and depends on the rate of utility as well as the amount of energy saving. Compared with the most wasteful control strategy (fixed setpoint - always on), adding a thermostat with adaptive – occupancy driven control strategy can lead to less than one year of payback period regardless of location.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from ...extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.
PDF
