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Balancing trade-offs between resource availability, landscape quality, property price and visual impact in feasibility assessment of wind farm development
Transitioning from fossil fuels to renewable energy sources (RE) is crucial for mitigating climate change and ensuring a sustainable future. Usually, the feasibility of the energy transition on the local scale is assessed by considering the technical and economic potentials of RE technologies, as well as their
environmental impact. However, the plans for the energy system transition often encounter local
opposition. Communities near proposed wind farms may express concerns about their visual impact, noise, or changes to their way of life. In this regard, identifying and understanding the trade-offs between different factors that may influence the local development of wind technologies is a nontrivial task.
Keywords: GIS analysis, machine learning, renewable energy
Task description:
1. Literature review on the factors which may have a local influence on the development of wind
farms.
2. Defining the geographical scope of the study (it is suggested to select the United Kingdom).
3. Data collection, preprocessing and GIS analysis of the factors. If required, clustering of the
factors.
4. Modelling the visual impact of wind turbines using available local population data, landscape
scenic value data and viewshed analysis tools.
5. Identification of relationships among the chosen factors: whether high resource availability
geospatially coincides with high visual impact or high property prices or other considered
aspects (e.g., existing infrastructure).
6. Interpretation of the results: what do the uncovered relationships mean for further resource
assessment steps? (possible simplification due to inference of a certain factor from others)
7. If time allows, building scenarios to analyse potential trade-offs between the factors. The
scenarios will be applied for a specific region defined as a geographical scope of the study.
However, it will be ensured that the approach developed in this project can be transferred to
other geographical regions.
Task description: 1. Literature review on the factors which may have a local influence on the development of wind farms. 2. Defining the geographical scope of the study (it is suggested to select the United Kingdom). 3. Data collection, preprocessing and GIS analysis of the factors. If required, clustering of the factors. 4. Modelling the visual impact of wind turbines using available local population data, landscape scenic value data and viewshed analysis tools. 5. Identification of relationships among the chosen factors: whether high resource availability geospatially coincides with high visual impact or high property prices or other considered aspects (e.g., existing infrastructure). 6. Interpretation of the results: what do the uncovered relationships mean for further resource assessment steps? (possible simplification due to inference of a certain factor from others) 7. If time allows, building scenarios to analyse potential trade-offs between the factors. The scenarios will be applied for a specific region defined as a geographical scope of the study. However, it will be ensured that the approach developed in this project can be transferred to other geographical regions.
The goal of this project is to create a model which will explore the relationships between different geospatially distributed factors that can be used in the feasibility assessment of the local development of wind farms. These factors include, but are not limited to, local social perception of RE technologies, land prices, visual impact of wind turbines including visibility and landscape scenic value, wind resource
availability, land use limitations, and existing grid infrastructure. The model will consider the potential visibility of wind turbines to the local population through a viewshed analysis.
Therefore, this interdisciplinary project will integrate Geographic Information Systems (GIS) analysis and machine learning techniques, building upon prior research conducted within the Chair. In contrast to previous research, the main novelty of the study is to add the property price and visual impact into the analysis of the feasibility of future wind technology development, which will be conducted in high spatial resolution.
The goal of this project is to create a model which will explore the relationships between different geospatially distributed factors that can be used in the feasibility assessment of the local development of wind farms. These factors include, but are not limited to, local social perception of RE technologies, land prices, visual impact of wind turbines including visibility and landscape scenic value, wind resource availability, land use limitations, and existing grid infrastructure. The model will consider the potential visibility of wind turbines to the local population through a viewshed analysis. Therefore, this interdisciplinary project will integrate Geographic Information Systems (GIS) analysis and machine learning techniques, building upon prior research conducted within the Chair. In contrast to previous research, the main novelty of the study is to add the property price and visual impact into the analysis of the feasibility of future wind technology development, which will be conducted in high spatial resolution.
Proposal_landscape4RE.pdf
