ENERGY DATA ANALYTICS FOR IMPROVED RESIDENTIAL SERVICE QUALITY AND ENERGY EFFICIENCY

Utility companies generally have an extensive customer base, yet their knowledge about individual households is small. This adversely affects both the development of innovative, household specific services and the utilities’ key performance indicators such as customer loyalty and profitability. With the goal to overcome this knowledge deficit, persuasive systems in the form of customer self-service applications and efficiency coaching portals are becoming the getaway of data exchange between utility and user. While improved customer interaction and the collection of customer data within respective information systems is an important step towards a service-oriented company, the immediate value generated from the collected data is still limited, mostly due to the small fraction of customers actually using such systems. We show how to utilize the knowledge gained from the sparse number of active web users in order to provide low-cost and large-scale insights to potentially all residential utility customers. We do so using machine-learning-based Green IT artifacts that allow for improving decision-making, effectiveness of energy audits, and conservation campaigns, thus ultimately increasing the customer value and adoption of related services. Moreover, we show that data from the publically available geographic information systems can considerably improve the decision quality

Empowering customer engagement by informative billing: a European approach

Programmes aimed at improving end-use energy efficiency are a keystone in the market strategies of leading distribution system operators (DSOs) and energy retail companies and are increasing in application, soon expected to become a mainstream practice. Informative services based on electricity meter data collected for billing are powerful tools for energy savings in scale and increase customer engagement with the energy suppliers enabling the deployment of demand response programmes helping to optimise distribution grid operation. These services are completely in line with Europe’s 2020 strategy for overall energy performance improvement (cf. directives 2006/32/EC, 2009/72/EC, 2012/27/EU). The Intelligent Energy Europe project EMPOWERING involves 4 European utilities and an international team of university researchers, social scientists and energy experts for developing and providing insight based services and tools for 344.000 residential customers in Austria, France, Italy and Spain. The project adopts a systematic iterative approach of service development based on envisaging the utilities’, customers’ and legal requirements, and incorporates the feedback from testing in the design process. The technological solution provided by the leading partner CIMNE is scalable open source Big Data Analytics System coupled with the DSO’s information systems and delivering a range of value adding services for the customer, such as: - comparison with similar households - indications of performance improvements over time - consumption-weather dependence - detailed consumption visualisation and breakdown - personalised energy saving tips - alerts (high consumption, high bill, extreme temperature, etc.) The paper presents the development approach, describes the ICT system architecture and analyses the legal and regulatory context for providing this kind of services in the European Community. The limitations for third party data access, customer consent and data privacy are discussed, and how these have been overcome with the implementation of the “privacy by design” principle is explained

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Climate Resilient & Equitable Water Systems Capital Scan

Climate change is affecting water supply, water management and the health of communities in U.S. cities. Changes in the timing, frequency and intensity of precipitation are placing stress on the built and natural systems that provide fresh water, manage storm water, and treat wastewater. Droughts are shrinking the water supply; heavy rainfall overburdens storm water systems, causing flooding in homes and neighborhoods. Low-income people and communities of color are often the most vulnerable to climate change, living in low-lying areas and lacking the resources to adapt and cope with challenges associated with these patterns.The cumulative impact of climate change on water resources not only leads to a reduction in water quality and the destruction of homes and property, but it can also be a threat to public health, force relocation of communities and cause economic harm.The vision of Kresge's Environment Program is to help communities build resilience in the face of climate change. We believe that cities are central to action on climate change and equity must be a fundamental part of our work in climate adaptation, climate mitigation and building social cohesion

The moderating influence of device characteristics and usage on user acceptance of smart mobile devices

This study seeks to develop a comprehensive model of consumer acceptance in the context of Smart Mobile Device (SMDs). This paper proposes an adaptation of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) model that can be employed to explain and predict the acceptance of SMDs. Also included in the model are a number of external and new moderating variables that can be used to explain user intentions and subsequent usage behaviour. The model holds that Activity-based Usage and Device Characteristics are posited to moderate the impact of the constructs empirically validated in the UTAUT2 model. Through an important cluster of antecedents the proposed model aims to enhance our understanding of consumer motivations for using SMDs and aid efforts to promote the adoption and diffusion of these devices

Achieving Green and Healthy Homes and Communities in America

In the Fall of 2010, the National Coalition to End Childhood Lead Poisioning contracted with the National Academy to develop and execute an online dialogue that would examine ways to increase the health, safety, and energy efficiency of low- to moderate-income homes. Since 1999, the National Coalition had worked to improve low- to moderate-income housing through the support and execution of home interventions that addressed multiple issues within a home at one time; an approach that often did not align with other traditional, single-issue housing assistance programs. By 2010, the National Coalition had taken on the leadership of the Green and Healthy Homes Initiative, a public-private partnership focused on integrating funding streams to improve low- to middle-income homes across the country.With plans to expand the GHHI's operations, the National Coalition partnered with the National Academy to conduct the National Dialogue on Green and Healthy Homes, a collaborative online dailogue in which participants were asked to identify challenges to, and innovative practices for, improving the health, safety and energy-efficiency of low- to moderate- income homes. The Dialogue was live from November 4-November 22, 2010, and collected 100 hundred ideas and 362 comments from 320 registered users. Over the course of its two and a half week duration, the Dialogue received more than 2,500 visits from over 1,100 people in 48 states and territories. Key FindingsBy reviewing the feedback received in the Dialogue, the Panel was able to make a number of recommendations on how the green and healthy homes community of practice could increase the health, safety and energy efficiency of homes across the country. These recommendations included: Conduct an evaluation of current housing standards to determine if they meet the Nation's health, safety, and energy efficiency needs; Develop a tiered performance standard for healthy, safe and energy efficient homes; Group government funding streams to better align programs with the comprehensive intervention approach; Develop a long-term funding strategy to support efforts after Recovery Act funding ends; and Educate government decisionmakers and the public on the importance of developing green and healthy homes and communities, and the work that supports that development

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