Effects of group performance feedback and goal-setting in an organisational energy intervention

End-user energy demand (EUED) in the workplace is affected by a complex interaction between behavioural, social, technological, regulatory and organisational factors. Designing technology-led interventions to encourage pro-environmental behaviour that acknowledge and support this complexity is a significant challenge. This paper discusses the design and evaluation of an EUED intervention implemented in the corporate infrastructure of a UK university administration department. Two intervention types, group feedback and group goal-setting were implemented. 16 participants were recruited and engaged with a four stage study (baseline, group feedback, group goal setting, and baseline) for a duration of 4 months. This study design allowed us to track clearly any changes in mid-term energy usage behaviour during and beyond intervention. Findings suggest that, surprisingly, participant energy consumption increased during the intervention period compared to baseline conditions. These results demonstrate that simple group-based behaviour change methods can be counter-productive in the workplace, illustrating the complex and unpredictable nature of intervention in this design space

"I'd want to burn the data or at least nobble the numbers": Towards data-mediated building management for comfort and energy use

In this paper, we explore the role of pervasive environmental sensor data in workplace building management. Current interactions between management and workplace occupants are limited by the gap between experiences of (dis)comfort (i.e. individual preferences and perceptions) and the rigid objectivity of organisational policies and procedures such as static setpoint temperatures for indoor spaces. Our hypothesis is that pervasive sensor data that captures the indoor climate can provide an effective platform from which to more successfully communicate about comfort and energy use. Through a qualitative study with building managers and occupants, we show that while data does not necessarily resolve these tensions, it provides an engaging forum for a more inclusive building management process, and we outline directions for taking a more conversational approach in the design of comfort and energy-use interventions for the workplace

An Occupancy Based Cyber-Physical System Design for Intelligent Building Automation

Cyber-physical system (CPS) includes the class of Intelligent Building Automation System (IBAS) which increasingly utilizes advanced technologies for long term stability, economy, longevity, and user comfort. However, there are diverse issues associated with wireless interconnection of the sensors, controllers, and power consuming physical end devices. In this paper, a novel architecture of CPS for wireless networked IBAS with priority-based access mechanism is proposed for zones in a large building with dynamically varying occupancy. Priority status of zones based on occupancy is determined using fuzzy inference engine. Nondominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the optimization problem involving conflicting demands of minimizing total energy consumption and maximizing occupant comfort levels in building. An algorithm is proposed for power scheduling in sensor nodes to reduce their energy consumption. Wi-Fi with Elimination-Yield Nonpreemptive Multiple Access (EY-NPMA) scheme is used for assigning priority among nodes for wireless channel access. Controller design techniques are also proposed for ensuring the stability of the closed loop control of IBAS in the presence of packet dropouts due to unreliable network links

Cost effective and Non-intrusive occupancy detection in residential building through machine learning algorithm

Residential and commercial buildings consume more than 40% of energy and 76% of electricity in the U.S. Buildings also emit more than one-third of U.S. greenhouse gas emissions, which is the largest sector. A significant portion of the energy is wasted by unnecessary operations on heating, ventilation, and air conditioning (HVAC) systems, such as overheating/overcooling or operation without occupants. Wasteful behaviors consume twice the amount of energy compared to energy-conscious behaviors. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control and monitor the HVAC and lighting system based on occupancy information. However, the complicated installation process of occupancy sensors and their long payback period have prevented consumers from adopting this technology in the residential sector. Hence, I explored a method to detect the presence of an occupant and utilize it to reduce energy wasting in residential buildings. Existing methods of occupancy detection often focus on directly measure occupancy information from environmental sensors. The validity of such a sensor network highly depends on the room configurations, so the approach is not readily transferrable to other residential buildings. Instead of direct measurement, the proposed scheme detects the change of occupancy in a building. The new scheme implements machine learning methods based on a sequence of human activities that happens in a short period. Since human activities are similar regardless of house floorplan, such an approach may lead to readily transferrable to other residential buildings. I explored three types of human activity sensor to detect door handle touch, water usage, and motion near the entrance, which are highly correlated with the change of occupancy. The occupancy change is not only based on one single human activity, it also depends on a series of human activities that happen in a short period, called event. As the events have different durations and cannot be readily applicable to existing machine learning models due to varying input matrix sizes. Hence, I devised a fixed format to summarize the event regardless of the total duration of the event. Then I used a machine learning model to identify the occupancy change based on the event data. The saving potential of occupancy driven thermostat is about 20 % of energy in residential buildings. However, the actual saving impact in any given house can vary significantly from the average value due to the large variety of residential buildings. Existing building simulation tools did not readily consider the random nature of occupancy and users’ comfort. For this reason, I explored a co-simulation platform that integrates an occupancy simulator, a cooling/heating setpoint control algorithm, a comfort level evaluator, and a building simulator together. I explored the annual energy saving impact of an occupancy-driven thermostat compare with a conventional thermostat. The simulation had been repeated in five U.S. cities (Fairbanks, New York City, San Francisco, Miami, and Phoenix) with distinctive climate zones

An approach to increasing awareness of IAQ

This project explores the approach to increasing the awareness of indoor air quality using a literature research to evaluate historical context, critiquing combined contaminants, reviewing fragrances, appraising awareness, resources and government policy, both UK and Worldwide. Two surveys were conducted with local authorities and Occupational Health and Safety (OHS) Practitioners in order to explore risk perception and awareness within a real world situation. The literature research and project activities raised further discussion points regarding the application of risk management, cost effective modelling, impact of body burden, the increase trend of scent marketing, understanding and influencing society risk perception and evaluation of the leadership of IAQ at local and government levels. This project highlights some key recommendations including the requirement to label products, particularly products like perfume who claim brand protection, to enable the consumer to understand the ingredients and make choices about their purchases; Funding for the development of bio-monitoring and multi-pollutant frameworks to build on existing silo contaminant research and create a harmonised and structured approach in understanding psychological and physiological impact interactions from a mixture of pollutants; And the establishment of an IAQ body to lead and engage stakeholders to deliver effective IAQ models. As a legacy of the project, the first accredited IOSH UK IAQ certificate and website was developed, implemented and appraised. This project is submitted as partial fulfilment of the requirements for the degree of Doctor of Professional Studies with an overall programme plan of ‘setting the agenda and raising awareness of IAQ within the UK’

Photovoltaic tests and applications project

The activities and accomplishments of the Photovoltaic Tests and Applications Project during the period April 1976 through June 1977 are summarized. Results of efforts to identify potential near-term photovoltaic applications and users are discussed, including the outcome of an extensive survey of Federal government agencies. The status of application experiments is presented. Various general engineering efforts are reported, including the design and construction of a photovoltaic Systems Test Facility. Efforts to develop a high efficiency 10 kVA self-commutated inverter and controller specifically designed for photovoltaic systems are also discussed. The results of a wide variety of activities in the area of photovoltaic measurements and standards are related. Documents generated by the Project during the reporting period are listed in an Appendix

The development of a real-time energy prediction framework in domestic buildings

The construction industry consumed 35% of worldwide energy, with domestic buildings accounting for 22%. Providing a healthy, positive environment in domestic buildings raised energy demand by around 80% in building operations, with thermal comfort accounting for about half of that increase. Furthermore, building energy consumption is 5 to 10 times greater than predictions given during the design phase. The discrepancy between the actual and intended design is called the performance gap. Although the term is widely used in the context of energy performance, it can also be found in indoor environmental parameters such as temperature, relative humidity, air quality, noise, and illumination. This thesis connects building performance simulation to building operational performance, focusing on real-time energy prediction for space heating in an indoor environment of domestic buildings. The work presented in this research is a technical implementation framework for examining the energy consumption of indoor space heating in real-time, focusing on energy-related thermal comfort conditions at the zone level. Unlike building performance simulation tools, The developed framework can be used beyond the design phase to encompass operations and assist in diagnosing and detecting building underperformance or performance discrepancy over time. Focusing on zone level can offer a greater understanding of the thermal state and energy usage of specific individual spaces, which can also assist in identifying performance disparity. Buildings with good indoor environmental quality are objectively assessed using simulation tools. However, the indoor environmental quality, especially thermal comfort, is experienced subjectively, making the building energy and thermal performance evaluation task challenging. The developed framework extends the use of the energy model to the operational stage by predicting thermal and energy performance based on indoor and outdoor environmental parameters. Moreover, using a parametric energy simulation and machine learning approach connected to an IoT sensor system enable users to identify thermal comfort conditions in the indoor environment and the amount of energy consumed for space heating. Finally, the research identified several lessons that can potentially inform and improve the existing domestic buildings, especially winter space heating. Following the framework, an innovative device was developed and validated using an experimental approach that focuses on real-time energy prediction of space heating. In this process, the experimental case studies' thermal comfort conditions and energy consumption were monitored and analysed to identify thermal-energy performance-related issues, also used for validating the proposed real-time energy prediction module