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

Smart water metering as a non-invasive tool to infer dwelling type and occupancy – Implications for the collection of neighbourhood-level housing and tourism statistics

The international rollout of advanced metering infrastructure (AMI) in the residential water supply sector affords tremendous benefits in driving water-use efficiencies, accurate billing and network management (e.g. leak detection). AMI, using ‘smart meters’ fitted at a dwelling level, record water consumption at high temporal resolution. Since water is typically only consumed when householders are present, these data could offer a non intrusive means of inferring dwelling occupancy patterns. These insights could have a range of benefits dependent upon the spatiotemporal scale and the intended application – our interest is in the potential of these data to identify dwelling type, specifically to identify dwellings that have occupancy patterns associated with tourism, such as second homes or short-term holiday rentals. We focus on these data in a UK context and draw on data rarely available for academic research. Our data relate to a sample of dwellings in Devon and Cornwall, South West England. They capture high-temporal resolution water consumption during Covid-19 ‘lockdown’ and ‘staycation’ periods, providing a unique opportunity to demonstrate that these data can reveal the unusually pronounced property-level occupancy trends evident during this period. We apply Non-Intrusive Occupancy Monitoring (NIOM) to extract dwelling-level occupancy status (occupied/unoccupied) on a day-by-day basis. We group properties according to their occupancy trends, inferring a set of properties that exhibit occupancy characteristics associated with tourism. We demonstrate that these show correspondence with underlying in dicators of tourism activity, drawn from AirDNA records of short-term tourist rental properties in this area. Ongoing global rollout of AMI means that these data will be routinely available at the dwelling level and we reflect on the benefits they could provide in generating near real time insights into dwelling occupancy. Drawing on our collaboration with the Office for National Statistics (the UKs national statistical institute) we outline the considerable potential that these data and approaches could offer in the collation of small area housing and tourism statistics

Smart-Meter-Datenanalyse für automatisierte Energieberatungen ("Smart Grid Data Analytics") - Schlussbericht

Kommunikationsfähige Stromzähler ermöglichen die Erfassung individueller Lastprofile mit hoher zeitlicher Auflösung (typischerweise in 15-Minuten-Intervallen). Projektgegenstand ist die Weiterentwicklung von Methoden des maschinellen Lernens, um aus Lastprofilen und zusätzlichen verbrauchs-relevanten Informationen (Wetter, soziodemographische Daten, Adressinformationen, usw.) automatisiert Merkmale von Haushalten abzuleiten, welche für eine individuelle und spezifische Energieberatung von Nutzen sind. Mit den im Rahmen des Projektes entwickelten Smart-Meter-Klassifikations-Verfahren konnten 38 Eigenschaften privater Haushalte mit zum Teil hoher Sicherheit (über 70%) aus Lastprofilen und zusätzlichen frei verfügbaren Daten unter Einhaltung von Datenschutzbestimmungen vorhergesagt werden. Neben Umständen der Lebenssituation (z.B. Familien, Rentner, Kinder, sozialer Status) lassen sich auch Energieeffizienz-Charakteristika (z.B. Heizungstyp, Hausalter und -grösse, Geräte im Haushalt) sowie Einstellungen (z.B. gegenüber erneuerbaren Energieträgern, Interesse an Ökostrom oder an Solaranlagen) mit den entwickelten Algorithmen abschätzen. Mit Hilfe der Projektresultate können autorisierte Energiedienstleister wirkungsvolle und skalierbare Effizienzkampagnen realisieren. Zugleich unterstützen die Projektresultate eine faktenbasierte Diskussion über die Vorteile (z.B. Steigerung der Energieeffizienz) und Kosten (z.B. Wirkung auf die Privatsphäre) solcher Verfahren.Smart electricity meters allow for capturing consumption data of individual households at a high resolution in time (typically at 15-minute intervals). The key objective of this project is to develop further and evaluate feature extraction and machine learning techniques for automatic identification of household properties based on electricity load profiles and additional consumption-related infor- mation (weather, socio-demographic data, holidays, etc.). The gained information shall render highly targeted and scalable energy efficiency services possible. The developed classification methods enable recognition of 38 household characteristics with accuracy of partially above 70%, based on smart meter load profiles and additional freely available data and under adherence to data privacy and security regulations. The characteristics describe inhabitants’ life situation (e.g., families, retirees, children, social status), energy efficiency (e.g., heating type, age and size of house, appliances in the household) as well as attitudes (e.g., toward renewable energy sources, interest on green electricity or solar panels). The project results will help authorized energy service providers in realization of effective and scalable energy efficiency campaigns. At the same time, the results support a factbased discussion of advantages (e.g., enhancement of energy efficiency) and costs (e.g., privacy implications) of such approaches

Automatic Socio-Economic Classification of Households Using Electricity Consumption Data

Interest in analyzing electricity consumption data of private households has grown steadily in the last years. Several authors have for instance focused on identifying groups of households with similar consumption patterns or on providing feedback to consumers in order to motivate them to reduce their energy consumption. In this paper, we propose to use electricity consumption data to classify households according to pre-defined“properties”of interest. Examples of these properties include the floor area of a household or the number of its occupants. Energy providers can leverage knowledge of such household properties to shape premium services (e.g., energy consulting) for their customers. We present a classification system – called CLASS – that takes as input electricity consumption data of a private household andprovidesasoutputtheestimatedvaluesofitsproperties. We describe the design and implementation of CLASS and evaluate its performance. To this end, we rely on electricity consumption traces from 3,488 private households, collected at a 30-minute granularity and for a period of more than 1.5 years. Our evaluation shows that CLASS – relying on electricity consumption data only – can estimate the majority of the considered household properties with more than 70 % accuracy. For some of the properties, CLASS’s accuracy exceeds 80%. Furthermore, we show that for selected properties the use of a priori information can increase classification accuracy by up to 11%

Have Green Teens Become Blue? Investigating changes and influences in adolescent attitudes towards electricity conservation

Global energy consumption has been steadily rising since the 1990s, with projections estimating a 56% increase in consumption by 2040 (EIA, 2013). Although Canada’s industrial sector accounts for the largest share of electricity consumption, the nation’s residential sector is also a significant source of consumption (NRCAN, 2016). As such, numerous studies have explored the influences on adult attitudes and behaviour towards electricity consumption and conservation (Wallis et al., 2015). Fewer studies, however, have investigated the attitudes and awarenesses of the electricity consumers of tomorrow; adolescents. Their role in the future of energy consumption warrants an investigation into the attitudes and awareness of this demographic with regards to electricity conservation. It is important to understand whether adolescents are in tune with current electricity conservation issues, if they are involved in any conservation practices, or if they are simply not interested. A decade after Lynes and Robinson’s initial 2007 investigation into Ontario adolescents’ attitudes, awareness, and behaviour towards electricity conservation, this study aims to investigate the changes in these areas. The initial study surveying 500 Ontario teens was replicated in 2017, and statistical tests comparing both studies were conducted using Excel and SPSS software. The comparison between 2007 and 2017 adolescent attitudes towards electricity conservation indicated an overall decrease in the level of interest and engagement. However, it is important to note that this disconnect is likely not due to a lack of concern, but rather a lack of understanding between electricity consumption and the issues adolescents report being concerned with (i.e. climate change and creating a sustainable future). In an attempt to comprehensively understand current attitudes towards electricity conservation, this study proposes a framework to investigate the affective, cognitive and conative (ACC) elements of adolescents’ attitudes towards electricity conservation, as well as the influences on the development of these attitudes. The proposed framework contributes an additional dimension to Bronfenbrenner’s Ecological System’s model, which outlines variables affecting the development of attitudes such as age, gender, parents, schools, and media. This framework contextualizes the ACC components that generate adolescent attitudes towards electricity conservation, within the internal and external influences on these components. Of the influences investigated, the level of parental education and sources of information were seen to have the most statistically significance influences in the 2017 survey. Teenagers of parents at either ends of the spectrum for levels of education (highest: second or graduate degree, lowest: some grade or high school) were seen to display higher levels of engagement and interest in electricity conservation. In addition, findings indicated that school remains an important influencer of these attitudes, whether EcoSchool certified or not. Statistically significant positive correlations were observed between 2017 adolescents’ affects towards electricity conservation (“I don’t really care” to “I am really interested…”) and their conation towards conservation behaviours (“doing very little” to “doing all [they] could possible do”). Weaker correlations were observed between teenagers’ cognition and conation, and cognition and affect. However, it is acknowledged that this study did not extensively explore participants’ cognition of electricity conservation. This study provides valuable insight from this demographic with regards to electricity conservation initiatives that would resonate with adolescents. The observed influences of parents, media, and school as sources of information recognize these as a valuable resource for promoting electricity-conservation attitudes and behaviours among this demographic. In addition, this study provides direction for pro-conservation programs to focus on developing affects and conations of adolescents towards this issue, to create more favourable attitudes towards electricity conservation

Pervasive Data Analytics for Sustainable Energy Systems

With an ever growing population, global energy demand is predicted to keep increasing. Furthermore, the integration of renewable energy sources into the electricity grid (to reduce carbon emission and humanity's dependency on fossil fuels), complicates efforts to balance supply and demand, since their generation is intermittent and unpredictable. Traditionally, it has always been the supply side that has adapted to follow energy demand, however, in order to have a sustainable energy system for the future, the demand side will have to be better managed to match the available energy supply. In the first part of this thesis, we focus on understanding customers' energy consumption behavior (demand analytics). While previously, information about customer's energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we then develop a versatile customer segmentation framework, track cluster changes over time, and identify key characteristics that define a cluster. Additionally, although household-level consumption is hard to predict, it can be used to improve aggregate-level forecasting by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the customer base. Furthermore, we develop an approach to model the uncertainty of future demand. In contrast to previous work that used computationally expensive methods, such as simulation, bootstrapping, or ensemble, we construct prediction intervals directly using the time-varying conditional mean and variance of future demand. While analytics on customer energy data are indeed essential to understanding customer behavior, they could also lead to breaches of privacy, with all the attendant risks. The first part of this thesis closes by exploring symbolic representations of smart meter data which still allow learning algorithms to be performed on top of them, thus providing a trade-off between accurate analytics and the protection of customer privacy. In the second part of this thesis, we focus on mechanisms for incentivizing changes in customers' energy usage in order to maintain (electricity) grid stability, i.e., Demand Response (DR). We complement previous work in this area (which typically targeted large, industrial customers) by studying the application of DR to residential customers. We first study the influence of DR baselines, i.e., estimates of what customers would have consumed in the absence of a DR event. While the literature to date has focused on baseline accuracy and bias, we go beyond these concepts by explaining how a baseline affects customer participation in a DR event, and how it affects both the customer and company profit. We then discuss a strategy for matching the demand side with the supply side by using a multiunit auction performed by intelligent agents on behalf of customers. The thesis closes by eliciting behavioral incentives from the crowd of customers for promoting and maintaining customer engagement in DR programs

Proceedings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting

At the EEDAL'15 conference 128 papers dealing with energy consumption and energy efficiency improvements for the residential sector have been presented. Papers focused policies and programmes, technologies and consumer behaviour. Special focus was on standards and labels, demand response and smart meters. All the paper s have been peer reviewed by experts in the sector.JRC.F.7-Renewables and Energy Efficienc