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Journal Article•DOI•

Energy intelligent buildings based on user activity: A survey

Tuan Anh Nguyen, Marco Aiello
01 Jan 2013-Energy and Buildings (Elsevier)-Vol. 56, pp 244-257
TL;DR: A novel survey of prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition is provided, determining the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads.
About: This article is published in Energy and Buildings.The article was published on 2013-01-01 and is currently open access. It has received 658 citations till now. The article focuses on the topics: Building automation & Energy consumption.

Summary (7 min read)

Jump to: [1. Introduction] – [2. Criteria and methods] – [2.1. Terminology] – [2.2. Inclusion criteria for studies] – [2.3. Inclusion criteria for systems] – [2.4. Search methods] – [2.5. Sectors and subsystems] – [2.6. Features for comparison] – [3. Energy intelligent buildings] – [3.1. The residential sector] – [3.2. The office sector] – [3.3. Other sectors] – [3.4. Discussion] – [4. Energy saving potential] – [Discussion] – [5. Activities taken into account] – [5.1. Real-time occupancy information] – [5.2. Real-time together with occupant's preferences] – [5.3. Prediction of occupancy patterns] – [5.4. Detailed activities] – [5.5. Discussion] – [6. Methodologies and technologies] – [6.1. Technologies] – [6.2. Methodologies] – [Methodologies and] – [6.3. Discussion] and [7. Conclusions and future perspectives on user activity as part of energy intelligent buildings]

1. Introduction

  • In the United States, buildings account for a surprisingly high 41% of energy consumption [1] .
  • But cutting energy consumption is also vital because it helps to preserve finite resources, lowers costs for businesses and consumers, and can be accomplished relatively quickly.
  • The general objective of a BECM system is to fulfill the occupants' requirements for comfort while reducing energy consumption during building operations.
  • In order to answer these questions, the authors here study prominent international projects on energy savings in buildings that are based on user activity as the key element of the system.
  • The actual comparisons of the studies using these features are presented in Section 3, Section 4, Section 5, and Section 6.

2. Criteria and methods

  • The field of intelligent buildings, intelligent homes, building automation systems (BMS) encompasses an enormous variety of technologies, across commercial, industrial, institutional and domestic buildings, including energy management systems and building controls.
  • The function of building management systems is to control, monitor and optimize building services such as lighting, heating, security, closed-circuit television (CCTV) and alarm systems, access control, audio-visual and entertainment systems, ventilation, filtration and climate control, etc., even time and attendance control and reporting (notably, staff movement and availability).
  • This often leads system developers to describe their systems variedly, for example, part of 'e-health' or 'home-care' subsystems.
  • Therefore, for this literature review, a set of selection criteria needs to be introduced to identify studies in which BECM systems based on user activity are covered.

2.1. Terminology

  • According to the research conducted by Wigginton and Harris [8] , there exist over 30 separate definitions of intelligence in relation to buildings while [9] discusses the best known academic and technical definitions of the term intelligent building.
  • The IBI definition focuses more on the benefit of the owners and their desired indoor environment, while the EIBG one concentrates on the benefit of the users and creating desired indoor environment for occupants.
  • With 10 key modules mentioned above, intelligent building can be considered as one which is 'designed and constructed based on an appropriate selection of 'Quality Environmental Modules' to meet the user's requirements by mapping with appropriate building facilities to achieve long term building values' [9] .
  • The common objective is that green buildings are designed to reduce the overall impact of the built environment on human health and the natural environment by efficiently using energy, water, and other resources, by protecting occupant health and improving employee productivity, or by reducing waste, pollution and environmental degradation.

2.2. Inclusion criteria for studies

  • The term 'energy intelligent buildings' refers to buildings equipped with technology that allows monitoring of their occupants and/or facilities designed to automate and optimize control of appliances, in particular, lights, HVAC system, and home appliances, with the goal of saving energy.
  • The term does not include reference to the technologies used to help people to overcome dependence and health problems.
  • For an intensive review of smart houses whose major targets are improving comfort, dealing with medical rehabilitation, monitoring mobility and physiological parameters, and delivering therapy, one should refer to [14] .
  • Many energy efficient buildings are designed to take advantage of (1) cutting energy demand including the use of designs, materials and equipment that are more energy efficient, (2) producing energy locally from renewable and otherwise wasted resources, and (3) using smart grids generating a surplus in some buildings and feeding it into the grid.
  • Nevertheless, these studies show limited evidence of innovation in the area of activity based computing, thus, they are not discussed in this survey.

2.3. Inclusion criteria for systems

  • The inclusion criteria for systems to be part of the survey are: 1. Systems which feature: wearable, portable, or implantable devices; mobile or stationary devices, such as sensors, actuators or other information and communication technologies (ICT) components embedded in the structural fabric of the intelligent buildings or everyday objects such as furniture, etc.
  • Systems which have components with 'activity recognition' or 'user behaviour' in the sense of context awareness or decision support properties.
  • Systems that perform actions to save energy and satisfy user comfort without human intervention or interaction.

2.4. Search methods

  • This literature review includes published work that has undergone peer review.
  • The authors search is restricted to articles in journals, chapters of periodicals and proceedings of conferences written in English and published between 1996 and 2012.
  • Some web sites describing prototypes, projects and systems or devices are also included.
  • Searches are conducted through IEEE Xplore, ACM Digital Library, or using the Google search engine.

2.5. Sectors and subsystems

  • The authors use "sector" to describe a global building type, such as an office or a single family, while "subsystem" to describe a group of appliances which have the same or similar functionality, such as lighting or HVAC system.
  • Many surveys on energy consumption, namely, [2, 15, 5, 16] , and [1] share the same figures about energy consumption distributions on sectors and subsystems.
  • The residential sector use significantly more energy than commercial buildings and are responsible for over 40% of total buildings' CO 2 emissions [5] .
  • Heating, cooling and lighting are the largest energy consumers in offices.
  • Retail's main energy consumers are HVAC and lighting.

2.6. Features for comparison

  • This review examines the studies based on several points of view.
  • Firstly, the authors examine the literature to determine the building types which their BECM systems most benefit from taking user activity into account.
  • Next, the energy saving potential of energy intelligent buildings based on user activity is investigated followed by the summary of the most important user activities and behaviours for BECM systems.
  • Last but not least, methodologies and technologies used for activity recognition or pattern prediction are analysed in order to show the most appropriate methods and technologies that are used for the sake of energy saving and user comfort in energy intelligent buildings.

3. Energy intelligent buildings

  • The authors select projects which have been deemed among the most significant from an international perspective and represent well the whole field, but the list does not have the ambition to be exhaustive.
  • Table 1 summarizes the energy intelligent buildings discussed, along with their focus on subsystems, i.e., HVAC, light, and plug loads.
  • The table also illustrates how energy intelligent buildings are classified by mean of sectors, i.e., residential, office, retail, and other.
  • It can be seen from the table that much attention has been given to residential and office sectors.

3.1. The residential sector

  • Many systems pay attention to residential sector, that takes up to 40% of energy consumption.
  • IDorm is an installation of gadgets, sensors and effectors in a student bedroom.
  • The overall objective of the E3SoHo project [27] is to bring about a significant reduction of energy consumption in European social housing by providing tenants with feedback on consumption and offering personalized advice for improving their energy efficiency.
  • The idea is based on the use of the Bayesian Network (BN) to predict the user's behaviour [30] .
  • The approach is to view the smart home as an intelligent agent that perceives its environment through the use of sensors, and can act upon the environment through the use of actuators.

3.2. The office sector

  • In the context of the intelligent buildings project [37] , a collaboration between a number of Swedish universities, Paul Davidsson and Magnus Boman produce a multi-agent system (MAS) that monitors and controls the lighting system in an office building.
  • In the context of the GreenerBuildings project [39] , the authors propose a recognition system that performs indoor activity recognition with the goal of providing input to a control strategy for energy savings in office buildings [40] .
  • The project is about utilizing harmoniously and most effectively all installed systems in a building, taking into account human factors and adapting the decisions in realtime as and when uncertainties occur.
  • The BODE project [51] at the University of California deals with occupancy measurement, modelling and prediction for building energy savings.
  • Their prototype is currently deployed in two graduate student offices on their campus, monitoring the occupancy information for each room and several switched devices (e.g. LCD displays, printer, speakers, desk lamp, microwave, coffee pot) [58] .

3.3. Other sectors

  • HosPilot [65] is a project started in 2009 that addresses the environmental aspects of hospitals.
  • The HosPilot aims to install and to tune an ICT-based system that will significantly reduce the energy consumption regarding lighting and HVAC in a hospital environment.
  • Three pilots is executed in hospitals (in the Netherlands, Spain and Finland) during normal operation.
  • HOMES program [47] chooses hotels and schools as representatives for the goal of higher energy efficiency while maintaining comfort.

3.4. Discussion

  • In summary, much attention has been given to residential and office sectors, while only few research pays attention to buildings of other types, namely, hospital, school, and public space.
  • Fig. 2 summarizes the number of the analysed studies on each sector of buildings.
  • This information is summarized in Table 2 , which is refined from Table 1 in order to provide a better view at how literature pays attention to the subsystems.
  • The impression is that while in Europe and the U.S. there is a growing concern about the preservation of the environment and more energy intelligent buildings are designed and researched.

4. Energy saving potential

  • With respect to HVAC systems, many of the works use Ener-gyPlus [66] , one of the premier tools for modelling the energy of buildings, to run their simulations, evaluating the potential energy savings of HVAC control based on occupancy prediction.
  • For the above, this survey does not intend to compare the energy saving potential between the studies.
  • In addition, other investigations also indicate that occupant satisfaction can be improved using dynamic HVAC adaptation concept, [68] and [69] .
  • Regarding lighting systems, a previous survey [70] evidences that up to 40% of the lighting electricity could be saved by adopting a combination of modern control strategies, such as daylight harvesting, occupancy sensing, scheduling and load shedding.
  • In addition, [32, 75] , and [30] choose to deal with the apportionment and prediction of energy consumption.

Discussion

  • In summary, while conceptual benefits of occupant-related building control approaches have shown energy saving benefits, their feasibility must be confirmed in real-life installations.
  • In parallel to optimizing energy consumption and performing automated adaptations, user comfort continues to be an essential success criteria for ICT-based solutions.
  • In addition, energy saving potential expressed in terms of percentage of saved kWh is convenient for an easier comparison.
  • Therefore, better evaluation metrics such as kWh/m 2 a should be used in order to have a fair evaluation of energy saving potential for energy intelligent buildings.

5. Activities taken into account

  • Occupant presence and behaviour in buildings have been shown to have large impacts on space heating, cooling and ventilation demand, energy consumption of lighting and space appliances, and building controls [4] .
  • Real-time occupancy information has long been used for control of various devices like artificial light, HVAC devices, etc. Recently, occupants' individual preferences have received growing interest in order to not only save energy but also to satisfy user comfort with respect to lighting system.
  • Thus, much research has been focused on predicting occupancy patterns for HVAC control.
  • Table 3 summarizes how reviewed studies take into account user activities and behaviours.

5.1. Real-time occupancy information

  • Several projects investigate and improve the way of using realtime occupant location data for lighting control.
  • In Europe, the authors of [50] consider lighting devices as instantaneous resume energy sinks (i.e., to the human eye, these devices switch power state instantaneously).
  • They use the room outlines as the relevant spatial zones, and take advantage of the faster update rates associated with fine-grained 3-D ultrasonic tracking to minimise the delay in turning on lights as a user enters.
  • The system attempts to conserve energy by automatically reducing the temperature for an occupant's room when the occupant is not in the building.
  • In the U.S., the authors in [73] propose using a belief network to improve the accuracy for occupancy detection within buildings.

5.2. Real-time together with occupant's preferences

  • Real-time location information alone is not enough for effective building energy and comfort management.
  • By contrast, if the timeout is longer than necessary, the lights are still on when the room is not occupied, which may result in energy wastage.
  • Thus occupants' individual lighting preferences should be taken into account.
  • In a similar approach, Chen et al. [61] propose a smart building control system that is able to keep track of workers' real-time location in an office and retrieve their personal preferences of lighting, cooling, and heating.
  • Instead of using occupant's preference, Newsham and Benjamin [56] use the total number of building occupants to forecast the power demand of the building in which a measure of building occupancy was a significant independent variable and increased the model accuracy.

5.3. Prediction of occupancy patterns

  • Temperature control has a long response time to power state changes and demands a predictive approach [50] .
  • Therefore, much research focuses on predicting occupancy patterns for HVAC control: AIM [29] , and [30] in Europe and the UK; IntelligentLighting [59] , IBMIntelligentBuilding [61] , ACHE [33] , the self-programming thermostat [35] , the smart thermostat [36] , OBSERVE [54] , and the research of Bing Dong and his colleagues [77] are conducted in the U.S.
  • The AIM system creates profiles of the behaviour of house inhabitants and through a prediction algorithm AIM is able to automatically control home appliances (mainly devices used for space heating/cooling, lighting) according to the users' habits.
  • Erickson et al. construct several models in [52, 54] , and [53] for predicting user mobility patterns in buildings.

5.4. Detailed activities

  • More detailed activities which are typical of building/home presence (e.g., working with or without PC, having a meeting, watching TV, using coffee maker) may affect comfort.
  • In addition, energy efficiency can be achieved if one can control plug loads (e.g., LCD, TV, multimedia entertainment devices, a coffee maker).
  • IDorm [74] is able to recognize three activities of a person, namely, sleeping, working, and entertaining.
  • Likewise, for the sake of controlling plug loads, the activity monitoring subsystem of SPOTLIGHT in [32] identifies who and which activities happen in the area of interest in the home environment.
  • The identified activities are watching TV, using coffee maker, and using living lamp/bedroom lamp.

5.5. Discussion

  • In summary, present energy intelligent buildings mostly use occupancy information for control strategies.
  • Real-time occupancy information is well suited to the lighting system.
  • It is estimated that energy expended on lighting could be cut by around 50% [50] .
  • Along these lines, BECM systems should be designed to reduce energy consumption under the constraint of satisfying user comfort in order to improve user acceptance of the system.
  • Temperature control has a long response time to state changes and demands a predictive approach.

6. Methodologies and technologies

  • Activity recognition has attracted increasing attention as a number of related research areas such as pervasive computing, intelligent environments and robotics converge on this critical issue.
  • It is also driven by growing real-world application needs in such areas as ambient assisted living and security surveillance.
  • The authors review here in this survey the most common technologies and approaches for indoor activity recognition for energy saving in building.

6.1. Technologies

  • Wireless sensor networks are the common approach of the various projects to address user activity recognition.
  • In the AIM Project, authors suggest to measure some physical parameters like temperature and light as well as user presence based on PIR sensors in each room of a house [29] .
  • In [56] , to gather data related to total building occupancy, wireless sensors are installed in a three-storey building in eastern Ontario, Canada comprising laboratories and 81 individual work spaces.
  • At the same time, a custom code that publishes the activity on the IP network senses computer-related activities of the user.

6.2. Methodologies

  • On the one hand, logical inference of sensor data approach is usually used to detect real-time occupancy.
  • Sensor network collects 24 h information about users presence/absence in each room of the house in a given monitoring period (i.e., week, month).
  • While Bayesian networks is used in [49] to support prediction of user behaviour patterns.
  • Whereas, in OBSERVE [54, 52] , Erickson et al. construct a multivariate Gaussian model, a Markov Chain model, and an agent-based model for predicting user mobility patterns in buildings by using Gaussian and agent based models.
  • The agent uses their fuzzy-logic-based incremental synchronous learning (ISL) system to learn and predict the user's needs, adjusting Real-time occupancy information should be used.

Methodologies and

  • Wireless sensor networks are today considered the most promising and flexible technologies.
  • Technologies the agent controller automatically, non-intrusively, and invisibly on the basis of a wide set of parameters (which is one requirement for ambient intelligence).
  • The authors use multiple sensory input to probabilistically infer occupancy.
  • By evaluating multiple sensory inputs, they determine the probability that a particular area is occupied.
  • In each office, PIR and telephone on/off hook sensors are used to determine if rooms are in occupied states.

6.3. Discussion

  • In building energy and user comfort management area, wireless sensor networks can play an important role by continuously and seamlessly monitoring the building energy use, which lays the foundation of energy efficiency in buildings.
  • The sensor network provides basic tools for gathering the information on user behaviour and its interaction with appliances from the home environment.
  • The sensor network can be implemented using several available technologies.
  • In addition, in contrast to other smart home applications, such as medical monitoring and security system, the domain of energy conservation can tolerate a small loss in accuracy in favour of cost and ease of use.
  • Therefore, an energy intelligent building might not require cameras or wearable tags that may be considered intrusive to the user.

7. Conclusions and future perspectives on user activity as part of energy intelligent buildings

  • Current situations show that building control is mainly done manually, from switching lights and appliances to control heating systems seasonally.
  • The authors claim that, in order to make buildings truly adaptable and maximize efficiency and comfort, they need to be more aware to the activities of the users and to the context of their environment.
  • In contrast to other smart home applications, such as medical monitoring and security system, the domain of energy conservation can tolerate a small loss in accuracy in favour of cost and ease of use.
  • In addition, their strong belief is that further building context information is needed for more effective BECM systems and that energy intelligent buildings should be ready to take advantage of Smart Grid.
  • Energy-intelligent buildings can respond to their actual use and changes in their environment.

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Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "Energy intelligent buildings based on user activity: a survey" ?

In order to answer these questions, the authors provide a novel survey of ctivity recognition prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition. The most promising and appropriate activity recognition technologies and approaches are discussed thus allowing us to conclude with principles and perspectives for energy intelligent buildings based on user activity. Through the survey, the authors determine the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i. e., HVAC, light, and plug loads.