Project description
Energy-efficient behaviour mechanisms to reduce energy poverty
Energy poverty is a pressing concern for households that grapple with the challenges of accessing affordable and dependable energy services. The EU has taken steps to tackle this issue by promoting energy-efficient behaviours. However, influencing user behaviour necessitates a deep understanding of the mechanisms behind it. The MSCA-funded DEEP project will determine whether analysing energy usage patterns can offer valuable insights for behavioural interventions aimed at alleviating energy poverty. Specifically, it will identify patterns in energy consumption behaviour, shedding light on potential cognitive biases that affect energy efficiency, and explore policy interventions to counter these cognitive biases. To achieve these goals, the project employs graph data modelling to analyse user-level data and quantify energy usage patterns.
Objective
Energy poverty households face insufficient access to affordable and reliable energy services, which can lead to adverse health effects and risky coping strategies. The EU made energy poverty a policy priority in the Clean energy for all Europeans package in 2019, and one of the objectives is to address energy poverty by promoting energy-efficient behaviors. However, intervening in users' behavioral decisions is difficult as it requires a deep understanding of the behavior patterns and a rational explanation of the behavior mechanism. This project will address a key question in energy poverty research: can behavior pattern analytics facilitate the decision-making of energy poverty? Hence, the main objective is to assess if data-driven analytics of energy use patterns can provide insight into behavioral interventions to address energy poverty. Through this project, the applicant plans to use three progressive analytics tasks to answer the question: (i) Identify the behavior patterns of energy consumption (Descriptive Analytics). (ii) Reveal potential cognitive biases that impede high energy efficiency (Prescriptive Analytics). (iii) Explore policy interventions against cognitive bias (Prediction Analytics). The project will analyze user-level data through Graph Data Modeling to quantify energy use patterns and behaviors, overcoming the limitations of the traditional method relying only on descriptive statistics. Host and secondment will complement the interdisciplinarity of the project from the perspectives of energy behavior (behavioral science) and cognitive visualization (computer science) respectively. The project will facilitate the applicant's long-term research goals with interdisciplinary knowledge, enhancing the development of his career as an independent researcher. Potential beneficiaries are energy poverty households and energy stakeholders. The proposal is in line with the Sustainable Development Goals (SDGs) and objectives of the H2020 program.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
76131 Karlsruhe
Germany