Dr. Alona Zharova
Profil
Forschungsthemen1
Plattform zur Förderung von Energieeffizienz und CO2-Reduktion in Privathaushalten
Quelle ↗Förderer: Andere inländische Stiftungen Zeitraum: 01/2025 - 12/2025 Projektleitung: Dr. Alona Zharova
Mögliche Industrie-Partner10
Stand: 26.4.2026, 19:48:44 (Top-K=20, Min-Cosine=0.4)
- 7 Treffer65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in EuropeP65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in Europe
- 7 Treffer65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in EuropeP65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in Europe
- 7 Treffer65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in EuropeP65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in Europe
- 7 Treffer65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in EuropeP65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in Europe
- 7 Treffer65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in EuropeP65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in Europe
- 5 Treffer65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in EuropeP65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in Europe
- 9 Treffer65.5%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in EuropeP65.5%
- Validating C. Elegans Healthspan Model for Better Understanding Factors Causing Health and Disease, to Develop Evidence Based Prevention, Diagnostic, Therapeutic and Other StrategiesP47.9%
- Climate-smart rewilding: ecological restoration for climate change mitigation, adaptation and biodiversity support in Europe
- 4 Treffer57.0%
- Zuwendung im Rahmen des Programms „exist – Existenzgründungen aus der Wissenschaft“ aus dem Bundeshaushalt, Einzelplan 09, Kapitel 02, Titel 68607, Haushaltsjahr 2026, sowie aus Mitteln des Europäischen Strukturfonds (hier Euro-päischer Sozialfonds Plus – ESF Plus) Förderperiode 2021-2027 – Kofinanzierung für das Vorhaben: „exist Women“T57.0%
- Zuwendung im Rahmen des Programms „exist – Existenzgründungen aus der Wissenschaft“ aus dem Bundeshaushalt, Einzelplan 09, Kapitel 02, Titel 68607, Haushaltsjahr 2026, sowie aus Mitteln des Europäischen Strukturfonds (hier Euro-päischer Sozialfonds Plus – ESF Plus) Förderperiode 2021-2027 – Kofinanzierung für das Vorhaben: „exist Women“
- 10 Treffer55.0%
- REGIO - Eine Kartierung der Entstehung und des Erfolgs von Kooperationsbeziehungen in regionalen Forschungsverbünden und Innovationsclustern. Determinanten der Entstehung und des Erfolgs von Kooperationsbeziehungen in regionalen ForschungsverbündenP55.0%
- REGIO - Eine Kartierung der Entstehung und des Erfolgs von Kooperationsbeziehungen in regionalen Forschungsverbünden und Innovationsclustern. Determinanten der Entstehung und des Erfolgs von Kooperationsbeziehungen in regionalen Forschungsverbünden
- 2 Treffer54.8%
- Sortenstrategien bei landwirtschaftlichen Nutzpflanzen zur Anpassung an den KlimawandelP54.8%
- Sortenstrategien bei landwirtschaftlichen Nutzpflanzen zur Anpassung an den Klimawandel
Publikationen24
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
Information Fusion · 11 Zitationen · DOI
A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households’ energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it allows for easy integration of new agents, enabling seamless functionality expansion, or the disabling of existing agents to tailor the system to specific needs. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.
European Journal of Operational Research · 7 Zitationen · DOI
Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · 7 Zitationen
SSRN Electronic Journal · 5 Zitationen · DOI
Environmental Data Science · 2 Zitationen · DOI
Abstract Transparent, understandable, and persuasive recommendations support the electricity consumers’ behavioral change to tackle the energy efficiency problem. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we extend a novel multi-agent approach by designing and implementing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models (i.e., k-nearest-neighbors, extreme gradient boosting, adaptive boosting, Random Forest, logistic regression, and explainable boosting machines). Since we want to help the user understand a single recommendation, we focus on local explainability approaches. In particular, we apply post-model approaches local, interpretable, model-agnostic explanation and SHapley Additive exPlanations as model-agnostic tools that can explain the predictions of the chosen classifiers. We further provide an overview of the predictive and explainability performance. Our results show a substantial improvement in the performance of the multi-agent system while at the same time opening up the “black box” of recommendations.
RePEc: Research Papers in Economics · 2 Zitationen · DOI
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.
Explainable Multi-Agent Recommendation System for Energy-Efficient Decision Support in Smart Homes
2022arXiv (Cornell University) · 1 Zitationen · DOI
Understandable and persuasive recommendations support the electricity consumers' behavioral change to tackle the energy efficiency problem. Generating load shifting recommendations for household appliances as explainable increases the transparency and trustworthiness of the system. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we provide agents with enhanced predictive capacity by including weather data, applying state-of-the-art models, and tuning the hyperparameters. Second, we suggest an Explainability Agent providing transparent recommendations. We also provide an overview of the predictive and explainability performance. Third, we discuss the impact and scaling potential of the suggested approach.
Izvestiya TINRO · 1 Zitationen · DOI
Seasonal changes of chlorophyll a profiles are traced over the Amur Bay (Peter the Great Bay, Japan Sea) in May-October, 2017 by means of oceanographic sonde-profiler equipped with fluorometer. Two principally different types of the vertical profiles are revealed, which were formed by different mechanisms of productivity: i) Chl a concentration had the maximum at the sea surface and decreased with the depth in the internal part of the bay occupied by the estuarine waters, and ii) Chl a concentration had the maximum below the seasonal pycnocline in the external part of the bay connected with the open sea. The highest Chl a concentration was observed in July-August for the estuarine type because of summer monsoon flood on the rivers, but in September for the marine type because of the coastal upwelling induced by monsoon winds change. Comparing these results with estimations of Chl a concentration made with the satellite data, insufficient correspondence is concluded for the external part of the bay, outside the estuarine zone, because the satellite data don’t reflect well the chlorophyll a in the subsurface layer and its seasonal variations. Thus, underestimation of real productivity and feeding ability of marine areas is available with the satellite data on chlorophyll a.
edoc Publication server (Humboldt University of Berlin) · 1 Zitationen · DOI
New Public Management unterstützt Universitäten und Forschungseinrichtungen dabei, in einem stark wettbewerbsorientierten Forschungsumfeld zu bestehen. Entscheidungen unter Unsicherheit, z.B. die Verteilung von Mitteln für den Forschungsbedarf und Forschungszwecke, erfordert von Politik und Hochschulmanagement, die Beziehungen zwischen den Dimensionen der Forschungsleistung und den resultierenden oder eingehenden Zuschüssen zu verstehen. Hierfür ist es wichtig, die Variablen der wissenschaftlichen Wissensproduktion auf der Ebene von Individuen, Forschungsgruppen und Universitäten zu untersuchen. Das Kapitel 2 dieser Arbeit analysiert die Ebene der Individuen. Es verwendet die Beobachtungen der Forscherprofile von Handelsblatt (HB), Research Papers in Economics (RePEc, hier RP) und Google Scholar (GS) als meist verbreitete Ranking-Systeme in BWL und VWL im deutschsprachigen Raum. Das Kapitel 3 liefert eine empirische Evidenz für die Ebene von Forschungsgruppen und verwendet die Daten eines Sonderforschungsbereichs (SFB) zu Finanzinputs und Forschungsoutput von 2005 bis 2016. Das Kapitel beginnt mit der Beschreibung passender Performanzindikatoren, gefolgt von einer innovativen visuellen Datenanalyse. Im Hauptteil des Kapitels untersucht die Arbeit mit Hilfe eines Zeit-Fixed-Effects-Panel- Modells und eines Fixed-Effects-Poisson-Modells den Zusammenhang zwischen finanziellen Inputs und Forschungsoutputs. Das Kapitel 4 beschäftigt sich mit dem Niveau der Universitäten und untersucht die Interdependenzstruktur zwischen Drittmittelausgaben, Publikationen, Zitationen und akademischem Alter mit Hilfe eines PVARX-Modells, einer Impulsantwort und einer Zerlegung der Prognosefehlervarianz. Abschließend befasst sich das Kapitel mit den möglichen Implikationen für Politik und Entscheidungsfindung und schlägt Empfehlungen für das universitäre Forschungsmanagement vor.
Research Square · DOI
Understanding that cities are main arenas of climate action, it remains unclear which cities should focus on what kind of action taking a global comparative lens. Recent contributions identified four different types of cities across seven world regions, while others specified a huge case study literature database on cities and climate change biased towards established, stagnant, and megacities, largely ignoring smaller, rapidly growing cities, mostly in the Global South. Here, we comprehensively assess climate issues, climate actions, and their main political entry points and feasibility challenges for 17 types of cities. For this, we rely on three different data modalities: a) a global database of more than 10000 cities and 16 different quantitative characteristics; b) AI assisted full-pdf analysis of more than 1200 pdfs on representative cities of 17 different types; and c) AI-assisted web-based analysis of representative cities and their challenges. While the first two steps enable evidence identification with medium to high confidence, the last steps allows to fill crucial gaps and debias the analysis, albeit subject to low confidence insights. We find that smaller and poorer cities in Africa and Asia are strongly motivated to develop WASH infrastructure, that rapidly growing and megacities will need to focus on future proof urban planning, and that established cities, mostly in Europe, North America and East Asia, but also cities in small island states aim to disentangle from costly gas and oil dependence in heating and transport sectors. Cities that aim for net-zero are co-motivated by a high quality of life.
Research Square · DOI
arXiv (Cornell University) · DOI
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.
arXiv (Cornell University) · DOI
A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households' energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it provides flexibility and allows for adjustments and further enhancements. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.
arXiv (Cornell University) · DOI
A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.
arXiv (Cornell University) · DOI
Smart Home technology is increasingly seen as a solution for improving household energy efficiency. However, its energy-saving potential depends largely on how consumers use the system. To explore how user perception and intention to use Smart Home can influence energy efficiency, we develop a research model combining the theory of planned behavior (TPB) and the norm activation model (NAM), based on a comprehensive literature review. We collect data by surveying users of Smart Home systems (N = 363) and apply a partial least squares structural equation model (PLS-SEM) extended by a Random Forest algorithm to capture both linear and non-linear causal relationships. Results show that personal norms, shaped by a sense of responsibility and awareness of environmental consequences, are the strongest predictors of energy-efficient smart home use. Social norms and attitudes also significantly contribute to the intention to use these systems efficiently. Moreover, past behavior strengthens the link between personal norms and behavioral intention, highlighting the role of habit in shaping energy-related actions. To maximize the energy-saving potential of Smart Homes, system design should focus on reinforcing personal moral norms, supporting long-term engagement through habit-forming features, delivering personalized feedback on environmental and financial outcomes, and embedding green automation defaults. Implementing policy mechanisms that financially reward household energy savings presents a powerful lever for reducing emissions through improved energy efficiency in residential buildings.
SSRN Electronic Journal · DOI
SSRN Electronic Journal · DOI
CRCmapgu
2016SSRN Electronic Journal · DOI
edoc Publication server (Humboldt University of Berlin) · DOI
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.
RePEc: Research Papers 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.
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Plattform zur Förderung von Energieeffizienz und CO2-Reduktion in Privathaushalten
university
Plattform zur Förderung von Energieeffizienz und CO2-Reduktion in Privathaushalten
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Plattform zur Förderung von Energieeffizienz und CO2-Reduktion in Privathaushalten
university
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Identität, Organisation und Kontakt aus HU-FIS.
- Name
- Dr. Alona Zharova
- Titel
- Dr.
- Fakultät
- Wirtschaftswissenschaftliche Fakultät
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- Wirtschaftsinformatik
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- 26.4.2026, 01:14:30