Activity-Based Recommendations for Demand Response in Smart Sustainable Buildings

The energy consumption of private households amounts to approximately 30% of the total global energy consumption, causing a large share of the CO2 emissions through energy production. An intelligent demand response via load shifting increases the energy efficiency of residential buildings by nudging residents to change their energy consumption behavior. This paper introduces an activity prediction-based framework for the utility-based context-aware multi-agent recommendation system that generates an activity shifting schedule for a 24-hour time horizon to either focus on CO2 emissions or energy cost savings. In particular, we design and implement an Activity Agent that uses hourly energy consumption data. It does not require further sensorial data or activity labels which reduces implementation costs and the need for extensive user input. Moreover, the system enhances the utility option of saving energy costs by saving CO2 emissions and provides the possibility to focus on both dimensions. The empirical results show that while setting the focus on CO2 emissions savings, the system provides an average of 12% of emissions savings and 7% of cost savings. When focusing on energy cost savings, 20% of energy costs and 6% of emissions savings are possible for the studied households in case of accepting all recommendations. Recommending an activity schedule, the system uses the same terms residents describe their domestic life. Therefore, recommendations can be more easily integrated into daily life supporting the acceptance of the system in a long-term perspective

Understanding User Perception and Intention to Use Smart Homes for Energy Efficiency: A Survey

The positive impact of Smart Homes on energy efficiency is heavily dependent on how consumers use the system after adoption. While the technical aspects of Smart Home systems and their potential to reduce energy usage is a focus of various studies, there is a limited consideration of behavioral psychology while designing systems for energy management. To investigate users' perception and intention to use Smart Homes to support energy efficiency, we design a research model by combining a theory of planned behavior and the norm activation model. We design a questionnaire and conduct a survey targeting current smart home users (over 350 responses). To analyze the survey results, we extend the partial least squares structural equation modeling (PLS-SEM) by a random forest algorithm. The findings suggest that personal norms have the strongest influence on behavioral intention to use Smart Homes for energy efficiency, followed by the ascription of responsibility. Furthermore, the results support the effects of attitudes, subjective norms, awareness of consequences, as well as the moderating effect of past behavior on the relationship between personal norms and behavioral intentions

Academic Ranking Scales in Economics: Prediction and Imputation

Estagniol Nicolas Louis, comte d'. Motion concernant le remplacement des professeurs des universités et autres fonctionnaires chargés de l’enseignement public qui désobéissant la loi du 26 décembre 1791, lors de la séance du 4 avril 1791. In: Archives Parlementaires de 1787 à 1860 - Première série (1787-1799) Tome XXIV - Du 10 mars 1791 au 12 avril 1791. Paris : Librairie Administrative P. Dupont, 1886. p. 540

PRACTICAL METHODS OF INTEGRATION OF SYSTEM DYNAMICS AND AGENT-BASED MODELING

The article in connected with the identification of practical methods of integration of System Dynamics and Agent-Based Modeling as a platform for creation of an Agent-Dynamic model. The analysis of combined models in different areas is made and methods of combining approaches are explore

Academic Ranking Scales in Economics

Publications are a vital element of any scientist’s career. It is not only the number of media outlets but aslo the quality of published research that enters decisions on jobs, salary, tenure, etc. Academic ranking scales in economics and other disciplines are, therefore, widely used in classification, judgment and scientific depth of individual research. These ranking systems are competing, allow for different disciplinary gravity and sometimes give orthogonal results. Here a statistical analysis of the interconnection between Handelsblatt (HB), Research Papers in Economics (RePEc, here RP) and Google Scholar (GS) systems is presented. Quantile regression allows us to successfully predict missing ranking data and to obtain a so-called HB Common Score and to carry out a cross-rankings analysis. Based on the merged ranking data from different data providers, we discuss the ranking systems dependence, analyze the age effect and study the relationship between the research expertise areas and the ranking performance

Is Scientific Performance a Function of Funds?

The management of universities demands data on teaching and research performance. While teaching parameters can be measured via student performance and teacher evaluation programs, the connection of research outputs and their grant antecedents is much harder to check, test and understand. This paper elicits the interdependence structure between third-party expenses (TPE), publications, citations and academic age. To describe the relationship, we analyze individual level data from a sample of professorships from a leading research university and a Scopus database for the period 2001 to 2015. Using estimates from a PVARX model, impulse response functions and a forecast error variance decomposition, we show that analyzing on the high aggregation level of universities does not reflect the behavior of its faculties. We explain the differences in relationship structure between indicators for social sciences and humanities, life sciences and mathematical and natural sciences. For instance, for mathematics and some fields of social sciences and humanities the relationship between the TPE and the number of publications is insignificant, however, the influence of the TPE on the number of citation is significant and positive that indicates the difference between quality and quantity of research outputs. The paper also proposes a visualization of the cooperation between faculties and research interdisciplinarity via the co-authorship structure among publications. We discuss the implications for policy and decision making and suggest recommendations for research management of universities