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

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A Framework to Use Public-Private Partnership for Smart City Projects

The concept of Smart City has been emerging as a strategic set of integrated initiatives encompassing infrastructures, technology and digital services for the purpose of enhancing the quality of life of citizens. However, the development and implementation of Smart City projects require considerable investments that are difficult to fund with traditional public finance. In this context, Public-Private-Partnerships (PPP) appear to be suitable solutions to overcome the shortage of public finance and cuts on public spending. However, the adoption of PPP forms for Smart City projects has not been fully explored and only experimentally applied so far. In order to promote the usage of PPP to finance Smart City initiatives, this paper proposes some PPP financial instruments and discusses the associated strengths and weaknesses. In particular, the use of Project Finance, Revenue Sharing and Social Impact Bonds are suggested as sound alternatives and suitable sources of financing for Smart City project

Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis

We describe a social game that we designed for encouraging energy efficient behavior amongst building occupants with the aim of reducing overall energy consumption in the building. Occupants vote for their desired lighting level and win points which are used in a lottery based on how far their vote is from the maximum setting. We assume that the occupants are utility maximizers and that their utility functions capture the tradeoff between winning points and their comfort level. We model the occupants as non-cooperative agents in a continuous game and we characterize their play using the Nash equilibrium concept. Using occupant voting data, we parameterize their utility functions and use a convex optimization problem to estimate the parameters. We simulate the game defined by the estimated utility functions and show that the estimated model for occupant behavior is a good predictor of their actual behavior. In addition, we show that due to the social game, there is a significant reduction in energy consumption

Influence of occupants’ behaviour on energy and carbon emission reduction in a higher education building in the UK

This article focuses on one of the case studies in the Carbon Brainprint research project funded by the Higher Education Funding Council for England (Chatterton, J., D. Parsons, J. Nicholls, P. Longhurst, M. Bernon, A. Palmer, F. Brennan, et al. 2015. “Carbon Brainprint – An Estimate of the Intellectual Contribution of Research Institutions to Reducing Greenhouse Gas Emissions.” Process Safety and Environmental Protection 96: 74–81). The UK total CO2e emissions in 2010 amounted to 582MtCO2e. It is estimated that non-domestic buildings and domestic buildings were responsible for 18% (106MtCO2e) and 28% (165MtCO2e) of these emissions, respectively. A case study method was used to investigative the opportunity of using occupants’ awareness and behavioural interventions to reduce energy use and carbon emissions in a non-domestic building of a higher education institution. An action research approach, informed by the theory of planned behaviour, was argued for this case study. It has demonstrated 20% savings in lighting, office equipment and catering energy use, largely through user awareness and behaviour change. If this level of saving were to be reflected throughout the non-domestic building stock it would represent an annual reduction in the order of 7MtCO2e in the UK. These figures relate specifically to non-domestic buildings. However, some of the techniques involved are directly transferable to domestic buildings, with the potential for further emission reductions

Enabling low-carbon living in new UK housing developments

Purpose: The purpose of this paper is to describe a tool (the Climate Challenge Tool) that allows house builders to calculate whole life carbon equivalent emissions and costs of various carbon and energy reduction options that can be incorporated into the design of new developments. Design/methodology/approach: The tool covers technical and soft (or lifestyle) measures for reducing carbon production and energy use. Energy used within the home, energy embodied in the building materials, and emissions generated through transport, food consumption and waste treatment are taken into account. The tool has been used to assess the potential and cost-effectiveness of various carbon reduction options for a proposed new housing development in Cambridgeshire. These are compared with carbon emissions from a typical UK household. Findings: The tool demonstrated that carbon emission reductions can be achieved at much lower costs through an approach which enables sustainable lifestyles than through an approach which focuses purely on reducing heat lost through the fabric of the building and from improving the heating and lighting systems. Practical implications: The tool will enable house builders to evaluate which are the most cost-effective measures that they can incorporate into the design of new developments in order to achieve the significant energy savings and reduction in carbon emissions necessary to meet UK Government targets and to avoid dangerous climate change. Originality/value: Current approaches to assessing carbon and energy reduction options for new housing developments concentrate on energy efficiency options such as reducing heat lost through the fabric of the building and improving the heating and lighting systems, alongside renewable energy systems. The Climate Challenge Tool expands the range of options that might be considered by developers to include those affecting lifestyle choices of future residents. © Emerald Group Publishing Limited

Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response

The rollout of smart meters raises the prospect that domestic customer electrical demand can be responsive to changes in supply capacity. Such responsive demand will become increasingly relevant in electrical power systems, as the proportion of weather-dependent renewable generation increases, due to the difficulty and expense of storing electrical energy. One method of providing response is to allow direct control of customer devices by network operators, as in the UK 'Economy 7' and 'White Meter' schemes used to control domestic electrical heating. However, such direct control is much less acceptable for loads such as washing machines, lighting and televisions. This study instead examines the use of real-time pricing of electricity in the domestic sector. This allows customers to be flexible but, importantly, to retain overall control. A simulation methodology for highlighting the potential effects of, and possible problems with, a national implementation of real-time pricing in the UK domestic electricity market is presented. This is done by disaggregating domestic load profiles and then simulating price-based elastic and load-shifting responses. Analysis of a future UK scenario with 15 GW wind penetration shows that during low-wind events, UK peak demand could be reduced by 8-11 GW. This could remove the requirement for 8-11 GW of standby generation with a capital cost of £2.6 to £3.6 billion. Recommended further work is the investigation of improved demand-forecasting and the price-setting strategies. This is a fine balance between giving customers access to plentiful, cheap energy when it is available, but increasing prices just enough to reduce demand to meet the supply capacity when this capacity is limited

An ARTMAP-incorporated Multi-Agent System for Building Intelligent Heat Management

This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach