Energy Information Systems: From the Basement to the Boardroom

A significant buildings energy reduction opportunity exists in the office sector, given that this market segment typically is an early adopter of new technology. There is a rising trend towards smart and connected offices through the internet of things (IoT) that provides new opportunities for operational efficiency and environmental sustainability practices. Leading commercial real estate companies have begun to shift from individual building automation systems (BAS) to partially integrated and automated systems such as energy information systems (EIS). In both the United States and India, organizations are seeking operational excellence, enhanced tenant relationships, and topline growth. Hence it is imperative to engage the executives with decision-making power, by tapping into their interest in sustainability, corporate social responsibility, and innovation. This expansion of interest can enable data-driven decisions, strong energy investments, and deeper energy benefits, and would drive innovation in this field. However, none of this would be possible without robust, consistent building energy information to provide visibility across all the levels of decision making, i.e. from the basement where the facilities staff take operational action to the boardroom where the executives make investment decisions. Price, security, and ease of use remain barriers to the adoption and pervasive use of promising EIS technologies in commercial office buildings. We believe that these barriers can be addressed through the development of ready, simplified, consistent, commercially available, low-cost EIS-in-a-box packages, that have a pre-defined set of hardware components and software features and functionality that are pertinent to a particular building sector. These simplified, sector-specific EIS packages can help to obviate the need for customization, and enhance ease of use, thereby enabling scale-up, in order to facilitate building energy savings. The EIS-in-a-box are adaptable in both U.S. and Indian office buildings, and potentially beyond these two countries

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Benchmarking Big Data Technologies for Energy Procurement Efficiency

The electrical power industry is undergoing radical change due to the push for renewable energy that makes energy supply less predictable. Smart meters along with analytics software can grant insights into customer-specific consumption and thereby enable a better match between the demand and supply side for an electric utility. However, the vast amount of allocatable smart metering data and complexity of analytics pose challenges to database system. We address the implementation of an analytics ap-proach to optimize customer portfolios, eventually preventing excess energy procurement. Using real-world and simulated data, we test the suitability of big data approaches as well as traditional relational database technology. Furthermore, we present solutions based on big data platforms and demonstrate their cost effectiveness and performance. Our findings suggest economic feasibility of big data solutions for large utilities. Small and medium-sized utilities are advised to invest in more cost-effective solutions such as cluster-based systems

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

Cognitive barriers during monitoring-based commissioning of buildings

Monitoring-based commissioning (MBCx) is a continuous building energy management process used to optimize energy performance in buildings. Although monitoring-based commissioning (MBCx) can reduce energy waste by up to 20%, many buildings still underperform due to issues such as unnoticed system faults and inefficient operational procedures. While there are technical barriers that impede the MBCx process, such as data quality, the focuses of this paper are the non-technical, behavioral and organizational, barriers that contribute to issues initiating and implementing MBCx. In particular, this paper discusses cognitive biases, which can lead to suboptimal outcomes in energy efficiency decisions, resulting in missed opportunities for energy savings. This paper provides evidence of cognitive biases in decisions during the MBCx process using qualitative data from over 40 public and private sector organizations. The results describe barriers resulting from cognitive biases, listed in descending order of occurrence, including: risk aversion, social norms, choice overload, status quo bias, information overload, professional bias, and temporal discounting. Building practitioners can use these results to better understand potential cognitive biases, in turn allowing them to establish best practices and make more informed decisions. Researchers can use these results to empirically test specific decision interventions and facilitate more energy efficient decisions

Supporting Decentralised Energy Management through Smart Monitoring Systems in Public Authorities

open access articleEnergy infrastructure in large, multi-site organisations such as municipal authorities, is often heterogeneous in terms of factors such as age and complexity of the technology deployed. Responsibility for day-to-day operation and maintenance of this infrastructure is typically dispersed across large numbers of individuals and impacts on even larger numbers of building users. Yet, the diverse population of stakeholders with an interest in the operation and development of this dynamic infrastructure typically have little or no visibility of energy and water usage. This paper explores the integration of utility metering data into urban management processes via the deployment of an accessible “smart meter” monitoring system. The system is deployed in three public authorities and the impact of the system is investigated based on the triangulation of evidence from semi-structured interviews and case studies. The research is framed from three perspectives: the bottom-up micro-level (individual and local), the top-down macro-level (organisation-wide and strategic) and intermediate meso-level (community-focused and operation). Evidence shows that improved communication across these levels enables a decentralisation and joining-up of energy management. Evidence points to the importance of reducing the cognitive load associated with monitoring systems. Better access to information supports more local autonomy, easier communication and cooperation between stakeholders and fosters the conditions necessary for adaptive practices to emerge