Advances to ASHRAE Standard 55 to encourage more effective building practice

ASHRAE Standard 55 has been evolving in recent years to encourage more sustainable building designs and operational practices. A series of changes address issues for which past design practice has been deficient or overly constrained. Some of the changes were enabled by findings from field studies of comfort and energy-efficiency, and others by new developments in the design- and building-management professions.  The changes have been influencing practice and spurring follow-on research.The Standard now addresses effects of elevated air movement, solar gain on the occupant, and draft at the ankles, each with several impacts on energy-efficient design and operation. It also addresses the most important source of discomfort in modern buildings, the large inter- and intra-personal variability in thermal comfort requirements, by classifying the occupants’ personal control and adaptive options in a form that can be used in building rating systems. In order to facilitate design, new computer tools extend the use of the standard toward direct use in designers’ workflow. The standard also includes provisions for monitoring and evaluating buildings in operation. This paper summarizes these developments and their underlying research, and attempts to look ahead

Integrating Smart Ceiling Fans and Communicating Thermostats to Provide Energy-Efficient Comfort

The project goal was to identify and test the integration of smart ceiling fans and communicating thermostats. These highly efficient ceiling fans use as much power as an LED light bulb and have onboard temperature and occupancy sensors for automatic operationbased on space conditions. The Center for the Environment (CBE) at UC Berkeley led the research team including TRC, Association for Energy Affordability (AEA), and Big Ass Fans (BAF). The research team conducted laboratory tests, installed99 ceiling fans and 12 thermostats in four affordable multifamily housing sites in California’s Central Valley, interviewed stakeholders to develop a case study, developed an online design tool and design guide, outlined codes and standards outreach, and published several papers.The project team raised indoor cooling temperature setpoints and used ceiling fans as the first stage of cooling; this sequencing of ceiling fans and air conditioningreducesenergy consumption, especially during peak periods, while providing thermal comfort.The field demonstration resulted in 39% measured compressor energy savings during the April–October cooling seasoncompared to baseline conditions, normalized for floor area. Weather-normalized energy use varied from a 36% increase to 71% savings, withmedian savings of 15%.This variability reflects the diversity in buildings, mechanical systems, prior operation settings, space types, andoccupants’ schedules,preferences, and motivations. All commercial spaces with regular occupancy schedules (and twoof the irregularly-occupied commercial spaces and one of the homes) showed energy savings on an absolute basis before normalizing for warmer intervention temperatures,and 10 of 13 sites showed energy savings on a weather-normalized basis. The ceiling fans provided cooling for one site for months during hot weather when the coolingequipment failed.Occupants reported high satisfaction with the ceiling fans and improved thermal comfort. This technology can apply to new and retrofit residential and commercial buildings

Optimizing Radiant Systems for Energy Efficiency and Comfort

Radiant cooling and heating systems provide an opportunity to achieve significant energy savings, peak demand reduction, load shifting, and thermal comfort improvements compared to conventional all-air systems. As a result, application of these systems has increased in recent years, particularly in zero-net-energy (ZNE) and other advanced low-energy buildings. Despite this growth, completed installations to date have demonstrated that controls and operation of radiant systems can be challenging due to a lack of familiarity within the heating, ventilation, and air-conditioning (HVAC) design and operations professions, often involving new concepts (particularly related to the slow response in high thermal mass radiant systems). To achieve the significant reductions in building energy use proposed by California Public Utilities Commission’s (CPUC’s) Energy Efficiency Strategic Plan that all new non-residential buildings be ZNE by 2030, it is critical that new technologies that will play a major role in reaching this goal be applied in an effective manner. This final report describes the results of a comprehensive multi-faceted research project that was undertaken to address these needed enhancements to radiant technology by developing the following: (1) sizing and operation tools (currently unavailable on the market) to provide reliable methods to take full advantage of the radiant systems to provide improved energy performance while maintaining comfortable conditions, (2) energy, cost, and occupant comfort data to provide real world examples of energy efficient, affordable, and comfortable buildings using radiant systems, and (3) Title-24 and ASHRAE Standards advancements to enhance the building industry’s ability to achieve significant energy efficiency goals in California with radiant systems. The research team used a combination of full-scale fundamental laboratory experiments, whole-building energy simulations and simplified tool development, and detailed field studies and control demonstrations to assemble the new information, guidance and tools necessary to help the building industry achieve significant energy efficiency goals for radiant systems in California

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

System-level key performance indicators for building performance evaluation

Quantifying building energy performance through the development and use of key performance indicators (KPIs) is an essential step in achieving energy saving goals in both new and existing buildings. Current methods used to evaluate improvements, however, are not well represented at the system-level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are typically only either measured at the whole building level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (COP)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that delivers a specific service to a building (e.g., space heating, lighting). The increasing installation of sensors and meters in buildings makes the evaluation of building performance at the system level more feasible through improved data collection. Leveraging this opportunity, this study introduces a set of system-level KPIs, which cover four major end-use systems in buildings: lighting, MELs (Miscellaneous Electric Loads, aka plug loads), HVAC (heating, ventilation, and air-conditioning), and SWH (service water heating), and their eleven subsystems. The system KPIs are formulated in a new context to represent various types of performance, including energy use, peak demand, load shape, occupant thermal comfort and visual comfort, ventilation, and water use. This paper also presents a database of system KPIs using the EnergyPlus simulation results of 16 USDOE prototype commercial building models across four vintages and five climate zones. These system KPIs, although originally developed for office buildings, can be applied to other building types with some adjustment or extension. Potential applications of system KPIs for system performance benchmarking and diagnostics, code compliance, and measurement and verification are discussed

Thermal comfort guidelines for production spaces within multi-storey garment factories located in Bangladesh

This research presents extensive field data on indoor thermal conditions along with workers' comfort votes taken at their workstations within three existing multi-storied garment factories during the three seasons (cool-dry, hot-dry and warm-humid) of Bangladesh. The main objective of the study was to observe the impact of thermal conditions on workers’ indoor thermal perception during each season of a year and from this identify thermal comfort guidelines (e.g. neutral temperatures, comfort ranges, preferred airspeeds and directions) to execute their production work comfortably. Subjective votes were collected from a total of 908 workers with the thermal data, physiological data and adaptive measures recorded simultaneously. Statistical analyses revealed that workers can accept a wider and relatively higher comfort range than the predicted band during cool-dry and hot-dry seasons, for instance, 22.7–29.1 °C and 22.3–30.4 °C respectively. A narrower comfort band (e.g. 28.7–30.9 °C), close to the predicted range, was found during the warm-humid season, which can be maintained by reducing radiant temperature and elevating airspeed. Further analyses indicated that workers prefer a mean airspeed of 0.3  m/s and comfort range of 0–3.0  m/s specific to their activities preferably from inlets located on south, north and east facades while upward and downward air movement, from for example ceiling fans, causes a rise of air temperature in the occupational zone and thermal discomfort. This research also suggested that the maximum distances of workstations from the ventilation inlets (windows) should be maintained at 12–18 m for sufficient cross ventilation, personal controls and adaptive opportunities to help maintain preferred thermal condition

Comfort driven adaptive window opening behaviour and the influence of building design

It is important to understand and model the behaviour of occupants in buildings and how this behaviour impacts energy use and comfort. It is similarly important to understand how a buildings design affects occupant comfort, occupant behaviour and ultimately the energy used in the operation of the building. In this work a behavioural algorithm for window opening developed from field survey data has been implemented in a dynamic simulation tool. The algorithm is in alignment with the proposed CEN standard for adaptive thermal comfort. The algorithm is first compared to the field study data then used to illustrate the impact of adaptive behaviour on summer indoor temperatures and heating energy. The simulation model is also used to illustrate the sensitivity of the occupant adaptive behaviour to building design parameters such as solar shading and thermal mass and the resulting impact on energy use and comfort. The results are compared to those from other approaches to model window opening behaviour. The adaptive algorithm is shown to provide insights not available using non adaptive simulation methods and can assist in achieving more comfortable and lower energy buildings

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking