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Acceptability analysis of wind power
In 2021, solar and wind power for the first time provided more than 10% of the world’s electricity [1]. This makes wind a major and strategic part of the mix to achieve the energy transition and a green economy. Despite broad public support for renewables in general, challenges in social acceptance for wind continue to occur regionally and locally. The opposition usually focuses on aspects such as wildlife safety, biodiversi-ty protection, noise, visibility and landscape impacts, and loss in property values [2, 3]. For the effective implementation of wind farms, it is crucial to understand and address these varying facets of social accept-ability. This study aims to assess the perspectives of residents living near wind farms across different Euro-pean regions, employing Multicriteria Satisfaction Analysis (MUSA) [4]. The objective is to devise region-specific strategies to enhance the acceptability of wind energy projects. Additionally, this research will ex-plore the relationship between socio-demographic and geographical characteristics of the residents and their acceptance of wind power, aiming to uncover underlying factors influencing the social acceptability of wind energy.
The aims of this thesis are multifaceted, encompassing: (1) the collection and descriptive analysis of ques-tionnaires from diverse regions across Europe; (2) the development of an algorithm for clustered MUSA; (3) the formulation of strategies to enhance the acceptability of wind power.
Aligned with these objectives, the research methodology is divided into several distinct tasks. Initially, a survey will be conducted at selected wind farm sites to gather KPI, essential for understanding variations among these sites. Subsequently, a descriptive analysis will be undertaken to explore correlations between different parameters of the survey participants and the wind farms in question. Following this, an in-depth exploration into the methodology of MUSA is required, with a focus on developing an effective clustering algorithm. This algorithm will differentiate participant satisfaction levels based on various characteristics. Lastly, the findings from this algorithm will be utilized to create diverse visualizations. These visualizations will assist in the development of targeted strategies aimed at improving the social acceptance of wind pow-er projects.
The aims of this thesis are multifaceted, encompassing: (1) the collection and descriptive analysis of ques-tionnaires from diverse regions across Europe; (2) the development of an algorithm for clustered MUSA; (3) the formulation of strategies to enhance the acceptability of wind power.
Aligned with these objectives, the research methodology is divided into several distinct tasks. Initially, a survey will be conducted at selected wind farm sites to gather KPI, essential for understanding variations among these sites. Subsequently, a descriptive analysis will be undertaken to explore correlations between different parameters of the survey participants and the wind farms in question. Following this, an in-depth exploration into the methodology of MUSA is required, with a focus on developing an effective clustering algorithm. This algorithm will differentiate participant satisfaction levels based on various characteristics. Lastly, the findings from this algorithm will be utilized to create diverse visualizations. These visualizations will assist in the development of targeted strategies aimed at improving the social acceptance of wind pow-er projects.
Based on the analysis results and thesis report an article in a peer-reviewed scientific journal is published.
Based on the analysis results and thesis report an article in a peer-reviewed scientific journal is published.