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

Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting

Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous empirical studies and a handful of review articles exist, there is surprisingly little quantitative analysis of the literature, most notably none that identifies the impact of factors on forecasting performance across the entirety of empirical studies. In this article, we therefore present a Meta-Regression Analysis (MRA) that examines factors that influence the accuracy of short-term electricity load forecasts. We use data from 421 forecast models published in 59 studies. While the grid level (esp. individual vs. aggregated vs. system), the forecast granularity, and the algorithms used seem to have a significant impact on the MAPE, bibliometric data, dataset sizes, and prediction horizon show no significant effect. We found the LSTM approach and a combination of neural networks with other approaches to be the best forecasting methods. The results help practitioners and researchers to make meaningful model choices. Yet, this paper calls for further MRA in the field of load forecasting to close the blind spots in research and practice of load forecasting.Comment: 8 pages, 3 figures, 7 table

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

Augmented Cross-Selling Through Explainable AI—A Case From Energy Retailing

The advance of Machine Learning (ML) has led to a strong interest in this technology to support decision making. While complex ML models provide predictions that are often more accurate than those of traditional tools, such models often hide the reasoning behind the prediction from their users, which can lead to lower adoption and lack of insight. Motivated by this tension, research has put forth Explainable Artificial Intelligence (XAI) techniques that uncover patterns discovered by ML. Despite the high hopes in both ML and XAI, there is little empirical evidence of the benefits to traditional businesses. To this end, we analyze data on 220,185 customers of an energy retailer, predict cross-purchases with up to 86% correctness (AUC), and show that the XAI method SHAP provides explanations that hold for actual buyers. We further outline implications for research in information systems, XAI, and relationship marketing

Overcoming Anchoring Bias: The Potential of AI and XAI-based Decision Support

Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals’ decision-making (e.g., by manipulating purchase decisions). Recent advances in Artificial Intelligence (AI) and the explanations of its decisions through explainable AI (XAI) have opened new opportunities for mitigating biased decisions. So far, the potential of these technological advances to overcome anchoring bias remains widely unclear. To this end, we conducted two online experiments with a total of N=390 participants in the context of purchase decisions to examine the impact of AI and XAI-based decision support on anchoring bias. Our results show that AI alone and its combination with XAI help to mitigate the negative effect of anchoring bias. Ultimately, our findings have implications for the design of AI and XAI-based decision support and IS to overcome cognitive biases

Addressing Learners\u27 Heterogeneity in Higher Education: An Explainable AI-based Feedback Artifact for Digital Learning Environments

Due to the advent of digital learning environments and the freedom they offer for learners, new challenges arise for students\u27 self-regulated learning. To overcome these challenges, the provision of feedback has led to excellent results, such as less procrastination and improved academic performance. Yet, current feedback artifacts neglect learners’ heterogeneity when it comes to prescriptive feedback that should meet personal characteristics and self-regulated learning skills. In this paper, we derive requirements from self-regulated learning theory for a feedback artifact that takes learners’ heterogeneity into account. Based on these requirements, we design, instantiate, and evaluate an Explainable AI-based approach. The results demonstrate that our artifact is able to detect promising patterns in data on learners\u27 behaviors and characteristics. Moreover, our evaluation suggests that learners perceive our feedback as valuable. Ultimately, our study informs Information Systems research in the design of future Explainable AI-based feedback artifacts that seek to address learners\u27 heterogeneity

Exactly Integrable Dynamics of Interface between Ideal Fluid and Light Viscous Fluid

It is shown that dynamics of the interface between ideal fluid and light viscous fluid is exactly integrable in the approximation of small surface slopes for two-dimensional flow. Stokes flow of viscous fluid provides a relation between normal velocity and pressure at interface. Surface elevation and velocity potential of ideal fluid are determined from two complex Burgers equations corresponding to analytical continuation of velocity potential at the interface into upper and lower complex half planes, respectively. The interface loses its smoothness if complex singularities (poles) reach the interface.Comment: 5 pages, 2 figures; submitted to Physics Letter

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

ECMO for COVID-19 patients in Europe and Israel

Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients