Unmasking the Role of Remote Sensors in Comfort, Energy and Demand Response

Abstract: In single-zone multi-node systems (SZMRSs), temperature controls rely on a single probe near the thermostat, resulting in temperature discrepancies that cause thermal discomfort and energy waste. Augmenting smart thermostats (STs) with per-room sensors has gained acceptance by major ST manufacturers. This paper leverages additional sensory information to empirically characterize the services provided by buildings, including thermal comfort, energy efficiency, and demand response (DR). Utilizing room-level time-series data from 1,000 houses, metadata from 110,000 houses across the United States, and data from two real-world testbeds, we examine the limitations of SZMNSs and explore the potential of remote sensors. We discovered that comfortable DR durations (CDRDs) for rooms are typically 70% longer or 40% shorter than for the room with the thermostat. When averaging, rooms at the control temperature's bounds are typically deviated around -3{\deg}F to 2.5{\deg}F from the average. Moreover, in 95% of houses, we identified rooms experiencing notably higher solar gains compared to the rest of the rooms, while 85% and 70% of houses demonstrated lower heat input and poor insulation, respectively. Lastly, it became evident that the consumption of cooling energy escalates with the increase in the number of sensors, whereas heating usage experiences fluctuations ranging from -19% to +25%. This study serves as a benchmark for assessing the thermal comfort and DR services in the existing housing stock, while also highlighting the energy efficiency impacts of sensing technologies. Our approach sets the stage for more granular, precise control strategies of SZMNSs.

• The paper demonstrates that remote sensors in SZMRHs uncover temperature discrepancies of about ±3°F, enhancing the evaluation of thermal comfort and energy performance.
• It employs sensor averaging and diagnostic metrics such as RC, RQ, and RK to identify limitations of single-zone thermostats and guide strategic sensor placement.
• The findings reveal that while sensor integration can boost cooling comfort by up to 45%, it may also alter HVAC energy consumption significantly, with variations from -19% to +25% in heating and cooling.

Unmasking the Role of Remote Sensors in Comfort, Energy, and Demand Response

Introduction

The paper "Unmasking the Role of Remote Sensors in Comfort, Energy, and Demand Response" explores the efficacy of utilizing remote sensors in single-zone multi-room houses (SZMRHs) to enhance thermal comfort, energy efficiency, and demand response. This study leverages a comprehensive dataset from ecobee, consisting of room-level time-series data from 1,000 homes and metadata from 110,000 homes across the US. The key focus is to evaluate temperature discrepancies traditionally overlooked by single-zone thermostats, diagnose potential root causes of these discrepancies, and assess the broader implications of integrating remote sensors within SZMRHs.

Prevalence and Impact of Remote Sensors

Remote sensors' availability is on the rise, with 67% of homes with ecobee thermostats using at least one additional sensor. Furthermore, 73% of households participating in demand response (DR) programs through the eco+ feature incorporate additional sensory data to evaluate thermal comfort.

Figure 1: Distribution of remote sensors across ecobee metadata. The vertical bars demonstrate the count of houses with a specific number of sensors, while the red line indicates the cumulative percentage, reflecting the proportion of the population with that many sensors or more.

Characterizing Temperature Variations

Temperature discrepancies present a notable challenge. The study shows a consistent thermostat deviation of about ±3°F from the setpoint. In some cases, rooms exhibit even more pronounced deviations, which affect comfort and energy efficiency.

Figure 2: Relative frequencies of temperature deviations from the setpoint in various rooms, represented by different colored lines. The grey area highlights the comfort zone, within which temperature fluctuations are considered comfortable for occupants. The COI values, detailed in the legend for each respective room, quantify the proportion of time temperatures were maintained within the comfort zone.

During demand response events, comfort levels deviate from expectations, with some rooms retaining comfort for 70% longer or 40% shorter periods than the thermostat-controlled room.

Evaluating Averaging Techniques

While averaging multiple sensor readings appears beneficial, leading to an average of 45% improvement in comfort during cooling operations, substantial deviations persist in SZMRHs. Even under averaging methodologies, the expected thermal comfort benchmarks aren't consistently achieved.

Figure 3: Relative frequencies of temperature deviations from the cooling setpoint in various rooms, represented by different colored lines.

Diagnosing Limitations

The paper's diagnostic analysis leverages thermal parameters such as the thermal time constant ($RC$), thermal resistance times heat input ($RQ$), and heating power input ($RK$). These diagnostics reveal common room deficiencies across homes. High Solar Gain affects 95% of homes, Low Heating Input at 85%, and Poor Insulation at 70%.

Figure 4: This histogram depicts the collective distribution of RC values (top), accompanied by boxplots for individual room distributions (bottom) for cooling and heating seasons. Markers indicated in light blue represent the RC values for the room where the thermostat is located.

Energy Implications of Remote Sensors

Adding remote sensors shows nuanced energy implications. Cooling consumption rises notably with more sensors, linking to increased spatial temperature sensing and possibly triggering more frequent HVAC activation. In contrast, heating shows fluctuations, with energy impacts ranging from -19% to +25% depending on the sensor count.

Figure 5: Estimated impact of 1°F in outdoor temperature on HVAC (combined) duty cycle. 95% confidence interval is shown using brackets.

Conclusion

This study underscores the complex impact of incorporating remote sensors into SZMRHs. Despite enhancing the potential for granular control and improved thermal comfort, these sensors also contribute to increased energy consumption, particularly cooling. The findings advocate for strategic placement and utilization of remote sensors to balance energy efficiency with comfort, informing future residential HVAC system deployments and retrofitting strategies. The empirical evidence lays groundwork for developing more effective, data-driven control strategies, highlighting rooms that require targeted interventions to mitigate thermal discrepancies.