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

Feature Identification for Non-Intrusively Extracting Occupant Energy-Use Information in Office Buildings

Detailed energy-use information of office buildings’ occupants is necessary to implement proper simulation/intervention techniques. However, acquiring accurate occupant-specific energy consumption in office buildings at low cost is currently a challenging task since existing intrusive load monitoring (ILM) technologies require a large capital investment to provide high-resolution electricity usage data for individual occupants. On the other hand, non-intrusive load monitoring (NILM) approaches have been proven as more cost effective and flexible approaches to provide energy-use information of individual appliances. Therefore, extending the concept of NILM to individual occupants would be beneficial. This paper proposes two occupancy-related energy-consuming features, delay interval and magnitude of power changes and evaluates their significances for extracting occupant-specific power changes in a non-intrusive manner. The proposed features were examined through implementing a logistic regression model as a predictor on aggregate energy load data collected from an office building. Hypotheses tests also confirmed that both features are statistically significant to non-intrusively derive individual occupants’ energy-use information. As the main contribution of this study, these features could be utilized in developing sophisticated NILM-based approaches to monitor individual occupant energy-consuming behavior. 

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting

Non-intrusive Occupant Load Monitoring in Commercial Buildings

Commercial buildings consume more than 20 percent of total energy use in the United States and they have the highest energy-use intensity and growth rate compared to other major sectors. Promoting energy-saving behaviors among occupants has recently been considered as the most cost-effective approach for reducing commercial building energy consumption, especially for reducing energy consumption of miscellaneous electric loads (MELs) due to direct control of occupants over MELs. Therefore, tracking MELs consumption and linking it with occupants’ energy-saving behavior is critical in intervening occupants’ energy-use behaviors. Currently, individual plug-load meters at an individual’s workspace are mainly used for tracking MELs in a commercial building. However, the implementation of this approach for full scale adoption requires a large initial investment on the part of the business. In addition, such an approach cannot assess occupants’ use on shared resources (e.g., lighting, shared office electronics). On the other hand, non-intrusive load-monitoring is considered a cost-effective and feasible tool to disaggregate building-level data for estimating appliance-specific energy consumption. Previous studies have suggested that adding occupancy sensing data to a load disaggregation process can help in economically estimating occupant-specific energy consumption. However, there is still a gap in properly linking appliance-specific energy consumption to occupants’ energy-use behaviors in commercial buildings. In response, this dissertation proposes an approach which tracks occupant-specific energy-use right after they enter to a building (entry event) and right before they leave a building (departure event); occupants’ behaviors at these events have a large impact on a building’s energy consumption. By utilizing density-based clustering and discriminant analysis, the approach couples occupancy information collected from Wi-Fi infrastructures with aggregated energy-load data to disaggregate load data down to the level of individual occupants. This critically helps understanding individual occupants’ energy-use behaviors in an economic manner and particularly allows to deliver tailored information through personalized feedback to an occupant who follows non-energy-saving behavior, to modify her energy actions to energy efficient behavior

Energy and Occupancy Data by SSC Group

Electric energy data

Non-intrusive Occupant Load Monitoring in Commercial Buildings

Commercial buildings consume more than 20 percent of total energy use in the United States and they have the highest energy-use intensity and growth rate compared to other major sectors. Promoting energy-saving behaviors among occupants has recently been considered as the most cost-effective approach for reducing commercial building energy consumption, especially for reducing energy consumption of miscellaneous electric loads (MELs) due to direct control of occupants over MELs. Therefore, tracking MELs consumption and linking it with occupants’ energy-saving behavior is critical in intervening occupants’ energy-use behaviors. Currently, individual plug-load meters at an individual’s workspace are mainly used for tracking MELs in a commercial building. However, the implementation of this approach for full scale adoption requires a large initial investment on the part of the business. In addition, such an approach cannot assess occupants’ use on shared resources (e.g., lighting, shared office electronics). On the other hand, non-intrusive load-monitoring is considered a cost-effective and feasible tool to disaggregate building-level data for estimating appliance-specific energy consumption. Previous studies have suggested that adding occupancy sensing data to a load disaggregation process can help in economically estimating occupant-specific energy consumption. However, there is still a gap in properly linking appliance-specific energy consumption to occupants’ energy-use behaviors in commercial buildings. In response, this dissertation proposes an approach which tracks occupant-specific energy-use right after they enter to a building (entry event) and right before they leave a building (departure event); occupants’ behaviors at these events have a large impact on a building’s energy consumption. By utilizing density-based clustering and discriminant analysis, the approach couples occupancy information collected from Wi-Fi infrastructures with aggregated energy-load data to disaggregate load data down to the level of individual occupants. This critically helps understanding individual occupants’ energy-use behaviors in an economic manner and particularly allows to deliver tailored information through personalized feedback to an occupant who follows non-energy-saving behavior, to modify her energy actions to energy efficient behavior

Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry

Over the years, AI has been utilized as a powerful tool to address complex challenges in the AEC industry. In the current AI practices, machines predominantly plan, manage, control, and optimize work without appropriately considering human-related input and preferences. However, architects, engineers, managers, clients, and other decision makers should consider their input into their work to better generate their desired ideas, prototypes, and solutions. In addition, significant decisions in the AEC industry are mainly reliant on the heuristic processes where assumptions are developed from past experience. However, the current level of AI is not able to properly deal with such human information and experience. This fact especially in large projects can result in a failure to properly utilize the full benefits of AI. Thus, human-centered AI is an essential need to help the machines understand and utilize human input for amplifying human abilities and reflecting realistic conceptions in the AEC industry. This paper presents the major aspects and applications of human-centered AI in the AEC industry and discusses the anticipated benefits and challenges of this technology. Human-centered AI, mainly via natural language processing and machine reading comprehension, can understand and learn from human interests, preferences, languages, and behaviors for providing human-centered environments, systems, and approaches that satisfy human interests and preferences. As the major benefits, human-centered AI is expected to result in architectural processing optimization, design and engineering capability enhancement, data driven project management, collaboration improvement, and safety enhancement. Personalization of human-centered AI and training its systems are considered as the major challenges in developing this technology in the AEC industry. In addition, AEC-specific guidelines and statements should be regulated in developing human-centered AI utilized in hazardous areas. Human-centered AI is anticipated to provide the highest level of human control in the current fast-growing automation of the AEC industry

Towards utilizing internet of things (IoT) devices for understanding individual occupants' energy usage of personal and shared appliances in office buildings

Energy consumption in office buildings highly depends on occupant energy-use behaviors and intervening these behaviors could function as a cost-effective approach to enhance energy savings. Current behavior-intervention techniques extensively rely on occupant-specific energy-use information at the workstation level and often ignore shared appliances. It is because an occupant typically has full responsibility for her workstation appliances energy consumption and shares the responsibility of the shared appliances energy consumption. However, understanding energy-use behavior of both workstation and shared appliances is necessary for applying appropriate behavior-intervention techniques. Despite this importance, there is still no practical and scalable method to capture personalized energy-use information of workstation and shared appliances since the conventional methods use plug-in power meters that are extremely expensive and difficult to maintain over long period of time. To address this gap, we propose a comprehensive occupant-level energy-usage approach which utilizes the data from the internet of things devices in office buildings to provide information related to energy-use behavior of workstation and shared appliances of each occupant in an economical and feasible manner. In particular, we introduce an energy behavior index which quantitatively compares individual occupants’ energy-consuming data to identify high energy consumers and inefficient behaviors. Results from an experiment conducted in an office building equipped with internet of things devices demonstrate the feasibility of the proposed approach to classify occupants to different energy-usage categories. Our proposed approach along with appropriate behavior-intervention techniques could be used to impact occupant energy-use behaviors

Extracting Occupants’ Energy-Use Patterns from Wi-Fi Networks in Office Buildings

Wi-Fi networks are currently considered as an efficient and economical tool for occupancy sensing in office buildings. Studies particularly indicated that these networks could be utilized to understand/predict occupants’ energy-use patterns. Despite the value that investigating this possibility could provide for the current research, it has not been well explored how energy-use pattern information could be extracted from Wi-Fi system information. In response, this study utilizes statistical analyses to investigate the correlation of Wi-Fi flows with miscellaneous electric loads (MELs) in office buildings. MELs account for more than one-third of office-building energy consumption and are the best representative of occupants’ energy-use patterns. In the pursuit of the objective, data from two offices were collected over a 3-month period of time. Results from the analyses show that an average 92 percent of MELs energy consumption could be predicted through the Wi-Fi flows in a building. This finding thereby demonstrates that occupants’ energy-use patterns are highly positively correlated to Wi-Fi flows in a building and accordingly, the information of Wi-Fi networks could be utilized to understand/interpret these patterns. This significantly contributes to the current body of research and can be used to support efforts into understanding/enhancing occupants’ energy-use behaviors. In addition, since Wi-Fi networks are a major subset of internet of things (IoT) hardware systems and IoT implementation for intelligent energy management in buildings significantly depends on occupant energy-use patterns, this research helps IoT-based efforts by displaying how these patterns could be extracted from IoT infrastructure