Supervised classification with interdependent variables to support targeted energy efficiency measures in the residential sector

This paper presents a supervised classification model, where the indicators of correlation between dependent and independent variables within each class are utilized for a transformation of the large-scale input data to a lower dimension without loss of recognition relevant information. In the case study, we use the consumption data recorded by smart electricity meters of 4200 Irish dwellings along with half-hourly outdoor temperature to derive 12 household properties (such as type of heating, floor area, age of house, number of inhabitants, etc.). Survey data containing characteristics of 3500 households enables algorithm training. The results show that the presented model outperforms ordinary classifiers with regard to the accuracy and temporal characteristics. The model allows incorporating any kind of data affecting energy consumption time series, or in a more general case, the data affecting class-dependent variable, while minimizing the risk of the curse of dimensionality. The gained information on household characteristics renders targeted energy-efficiency measures of utility companies and public bodies possible

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

A Decision Support System for Photovoltaic Potential Estimation

With knowledge on the photovoltaic potential of individual residential buildings, solar companies, energy service providers and electric utilities can identify suitable customers for new PV installations and directly address them in renewable energy rollout and maintenance campaigns. However, many currently used solutions for the simulation of energy generation require detailed information about houses (roof tilt, shading, etc.) that is usually not available at scale. On the other hand, the methodologies enabling extraction of such details require costly remote-sensing data from three-dimensional (3D) laser scanners or aerial images. To bridge this gap, we present a decision support system (DSS) that estimates the potential amount of electric energy that could be generated at a given location if a photovoltaic system would be installed. The DSS automatically generates insights about photovoltaic yields of individual roofs by analyzing freely available data sources, including the crowdsourced volunteered geospatial information systems OpenStreetMap and climate databases. The resulting estimates pose a valuable foundation for selecting the most prospective households (e.g., for personal visit and screening by an expert) and targeted solar panel kit offerings, ultimately leading to significant reduction of manual human efforts, and to cost-effective personalized renewables adoption

INCENTIVES TO GO GREEN: AN EMPIRICAL INVESTIGATION OF MONETARY AND SYMBOLIC REWARDS TO MO-TIVATE ENERGY SAVINGS

Green information systems have been shown to contribute to environmental sustainability and help to prevent associated problems. Private households account for 25% of primary energy consumption in western countries, and therefore hold a great potential to curb the use of fossil fuels and prevent cli-mate change. As such, green information systems should not focus solely on the organizational con-text, but also target a single individual’s behaviour in their home. Personal information systems (e.g., web portals) can achieve this focus, however, need to be actively used to produce effects. System us-age can be effectively motivated through incentives, and therewith contribute to positive outcomes. Incentives are either monetary or non-monetary and can be implemented in different scales. In a large field experiment (n= 2,355), with real energy customers of a utility company, we tested the effective-ness of different types and sizes of incentive in motivating active system usage. We show that incen-tives significantly increased system usage of participants, and additionally increased energy savings. However, monetary incentives were not necessarily superior to non-monetary incentives

Household classification using annual electricity consumption data

Introduction: The knowledge about household properties (such as number of inhabitants, living area, heating type, etc.) is highly desirable for utility companies to pave the way to targeted energy efficiency programs, products and services. Raising individual household data via surveys or purchasing it is expensive and time consuming, and often only a small fraction of customers participate. Recently, data mining methods have been developed to automatically infer house-hold characteristics from smart meter consumption data. However, the slow smart metering rollout hampers practical implementation of these methods in many countries. In this work, we present a machine learning approach that reveals household properties from conventional annual electricity consumption data currently available at a large scale

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

A Multi-Criteria Vendor Selection and Order Allocation GDSS using a Mixed Alternative and Value Focused Thinking Approach

Vendor selection and order quantity assignment plays a central role in the purchasing activity of manufacturing and trading companies. Evaluation of product and service suppliers for procurement planning requires, on the one hand, accurate identification and exploration of all decision relevant parameters. On the other hand, effective agreements must tackle all parties’ rights, obligations, interests, motives, and values, which are usually conflicting in nature. In this chapter, the authors address the problem of expert group structuring and formalization of participant competences by distinguishing among the a-, ß-, and ?-level experts responsible for the value system establishment, alternatives assessment and auxiliary objects evaluation, respectively. Experts can belong to more than one task community. The triples of a-, ß-, and ?-voting power indices are assigned to the individuals depending on their competence/authority. Moreover, the presented Multi-Criteria Decision Analysis (MCDA)-based framework facilitates selecting appropriate suppliers by the distributed expert groups and improves the quality of order allocation decisions. The usefulness of the proposed approach is demonstrated for the fuel oils and crops purchasing activities in the trading department of Raiffeisen Westfalen Mitte eG in Germany. </jats:p

Smart-Meter-Datenanalyse für massenmarkttaugliche Energiedienstleistungen

Intelligente Stromzähler ermöglichen die Erfassung von Lastprofilen einzelner Haushalte. Mit dem Einsatz von Analyse-Verfahren aus dem Bereich des maschinellen Lernens können Energieversorger aus Smart-Meter-Daten detaillierte Kundeninformationen (z.B. Heizungstyp oder Anzahl der Kinder) automatisch ableiten, die als Grundlage für eine personalisierte Energieberatungsleistung und zur Optimierung der Vertriebsaktivitäten dienen

A Multicriteria Multilevel Group Decision Method for Supplier Selection and Order Allocation

Supplier selection is an integral part of supply chain management (SCM). It plays a prominent role in the purchasing activity of manufacturing and trading companies. Evaluation of vendors and procurement planning requires simultaneous consideration of tangible and intangible decision factors, some of which may conflict. A large body of analytical and intuitive methods has been proposed to trade off conflicting aspects of realism and optimize the selection process. In the large companies the fields of decision makers’ (DMs) expertise are highly distributed and DMs’ authorities are unequal. On the other hand, the decision components and their interactions are very complex. These facts restrict the effectiveness of using the existing methods in practice. The authors present a multicriteria decision analysis (MCDA) method which facilitates making supplier selection decisions by the distributed groups of experts and improves quality of the order allocation decisions. A numerical example is presented and applicability of the proposed algorithm is demonstrated in the Raiffeisen Westfalen Mitte, eG in Germany