Feedback seeking as an active, goal-oriented behavior – a psychological reframing of energy consumption feedback

In the last decade the upcoming of the new digital metering technology combined with communication and information technologies caused a new wave of research on feedback and energy efficiency. In difference to earlier feedback studies, several field trials with sample sizes of several hundred up to thousands of households have been initiated in the European context in parallel. High expectations have been sowed from reviews on existing feedback research. Rather surprisingly the results in energy savings caused by feedback systems incorporating smart metering technology turned out to drag behind the high expectations. This doctoral thesis intends to line out an existing blind spot within the energy feedback research by highlighting the notion of an active recipient pursuing own goals and develop own strategies what to do with feedback. Findings and modelling from feedback research of organizational and social psychology is transferred to energy feedback research and forms the framework of a series of studies analysing empirical data from two large one-years-trials with feedback based on smart metering technologies. Major attention is given to the general concepts introduced in the theoretical frameworks: 1) Do individuals set goals for feedback use? If they do so, how are they are linked with each other – is there empirical evidence for multiple goal profiles? 2) Are the different goals determining the feedback seeking behavior? 3) Is there any empirical evidence that individuals proactively seek feedback information in a web-based feedback system? Do goals for feedback use have any predictive power for the feedback seeking behavior? 4) What is the effect on consumption, if different feedback seeking behaviours are identified, what conclusions in relation to the theoretical framework can be made

Project OASIS: Optimizing Aquaponic Systems to Improve Sustainability

Started in Fall 2015, Project OASIS (Optimizing Aquaponic Systems to Improve Sustainability) is an interdisciplinary capstone project with the goal of designing a sustainable and affordable small-scale aquaponic system for use in developing nations to tackle the problems of malnutrition and food insecurity. Aquaponics is a symbiotic relationship between fish and vegetables growing together in a recirculating system. The project’s goals were to minimize energy consumption and construction costs while using universally available materials. The computational fluid dynamics (CFD) software OpenFOAM was used to create transient and steady-state models of fish tanks to visualize velocity profiles, streamlines, and particle movement. CFD and small scale experiments showed vertical manifolds were more efficient than horizontal inlets. The components’ layout was analyzed to minimize head losses and airlifts were used instead of traditional water pumps. Full-scale research and traditional systems were constructed for side-by-side comparison of biological and energy factors. Flow improvements and use of air-lift pumps dropped energy consumption 40% when compared to a traditional system of the same size. Using local and recycled materials where possible decreased the cost of the UNH pilot system by 27%. The team also partnered with Forjando Alas, a non-profit in Uvita, Costa Rica. During a January 2016 assessment trip, four members spent a week gathering data and building relationships with the community to develop a user-centered design. Project OASIS also successfully competed in two entrepreneurship competitions this year

Relying on storage or ICT? How to maintain low voltage grids' stability with an increasing feed-in of fluctuating renewable energy sources

Since the beginning of the new century our electricity system is changing rapidly. Distributed energy resources, such as wind or solar energies are becoming more and more important. These energies are producing fluctuating electricity, which is fed into low voltage distribution grids. The resulting volatility complicates the exact balancing of demand and supply. These changes can lead to distribution grid instabilities, damages of electronic devices or even power outages and might therefore end in deadweight losses affecting all electricity users. A concept to tackle this challenge is matching demand with supply in real-time, which is known as smart grids. In this study, we focus on two smart grids' key components: decentralized electricity storages and smart meters. The aim of this study is to provide new insights concerning the low diffusion of smart meters and decentralized electricity storages and to examine whether we are facing situations of positive externalities. During our study we conducted eight in-depth expert interviews. Our findings show that the diffusion of smart meters as well as decentralized electricity storages is widely seen as beneficial to society. This study identifies the most important stakeholders and various related private costs and benefits. As private benefits are numerous but widely distributed among distinct players, we argue that we face situations of positive externalities and thus societal desirable actions are omitted. We identify and discuss measures to foster diffusion of the two studied smart grid key components. Surprisingly, we find that direct interventions like subsidies are mostly not seen as appropriate even by experts from industries that would directly benefit from them. As the most important point, we identified well-designed and clearly defined regulatory and legal frameworks that are free of contradictions. --smart meter,decentralized electricity storage,smart grid,externality

Assessing the impact of driving behavior on instantaneous fuel consumption

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Despite the recent technological improvements in vehicles and engines, and the introduction of better fuels, road transportation is still responsible for air pollution in urban areas due to the increasing number of circulating vehicles, and their relative travelled distances. We develop a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables such as Engine Fuel Rate, Speed, Mass Air Flow, Absolute Load, and Manifold Absolute Pressure, all of them obtained from the vehicle’s Electronic Control Unit (ECU). Our platform is able to assist drivers in correcting their bad driving habits, while offering helpful recommendations to improve fuel economy. In this paper we will demonstrate through data mining, to what extent does the driving style really affect (negatively or positively) the fuel consumption, as well as the increase or reduction of greenhouse gas emissions generated by vehicles.This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01Meseguer Anastasio, JE.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2015). Assessing the impact of driving behavior on instantaneous fuel consumption. IEEE. https://doi.org/10.1109/CCNC.2015.7158016

Artificial Intelligence in Energy Demand Response: A Taxonomy of Input Data Requirements

The ongoing energy transition increases the share of renewable energy sources. To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide flexibility is demand response (DR). Research already reflects several approaches of artificial intelligence (AI) for DR. However, these approaches often lack considerations concerning their applicability, i.e., necessary input data. To help putting these algorithms into practice, the objective of this paper is to analyze, how input data requirements of AI approaches in the field of DR can be systematized from a practice-oriented information systems perspective. Therefore, we develop a taxonomy consisting of eight dimensions encompassing 30 characteristics. Our taxonomy contributes to research by illustrating how future AI approaches in the field of DR should represent their input data requirements. For practitioners, our developed taxonomy adds value as a structuring tool, e.g., to verify applicability with respect to input data requirements

The Influence of Experimental and Computational Economics: Economics Back to the Future of Social Sciences

Economics has been a most puzzling science, namely since the neoclassical revolution defined the legitimate procedures for theorisation and quantification. Its epistemology has based on farce: decisive tests are not applied on dare predictions. As a consequence, estimation has finally been replaced by simulation, and empirical tests have been substituted by non-disciplined exercises of comparison of models with reality. Furthermore, the core concepts of economics defy the normally accepted semantics and tend to establish meanings of their own. One of the obvious instances is the notion of rationality, which has been generally equated with the apt use of formal logic or the ability to apply econometric estimation as a rule of thumb for daily life. In that sense, rationality is defined devoid of content, as alien to the construction of significance and reference by reason and social communication. The contradictory use of simulacra and automata, by John von Neumann and Herbert Simon, was a response to this escape of economic models from reality, suggesting that markets could be conceived of as complex institutions. But most mainstream economists did not understand or did not accept these novelties, and the empirical inquiry or the realistic representation of the action of agents and of their social interaction remained a minor domain of economics, and was essentially ignored by canonical theorizing. The argument of the current paper is based on a survey and discussion of the twin contributions of experimental and computational economics to these issues. Although mainly arising out of the mainstream, these emergent fields of economics generate challenging heuristics as well as new empirical results that defy orthodoxy. Their contributions both to the definition of the social meanings of rationality and to the definition of a new brand of inductive economics are discussed.

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl