Geospatial Analysis for Renewable Energy Site Selection

Geospatial analysis has become a cornerstone for identifying optimal locations for renewable energy installations by integrating spatial data, environmental constraints and socio-economic factors. At its core lies the use of Geographic Information Systems (GIS) to assemble layers of information such as solar irradiation, wind speed, topography, land cover and proximity to transmission networks. Remote sensing data provide high-resolution inputs on surface characteristics and resource availability, while digital elevation models refine assessments of slope and aspect. Multi-criteria decision-making frameworks overlay technical, environmental, economic and social criteria, often employing weighting schemes to reflect policy priorities or stakeholder preferences. Fuzzy logic and analytic hierarchy techniques accommodate uncertainty in parameter estimates, yielding continuous suitability indices rather than binary acceptability maps. Advances in machine learning and big-data analytics further enable the assimilation of meteorological records, crowdsourced geographic information and network topology into dynamic models that support regional and national planning. The approach is applicable to solar farms, wind parks, hydroelectric sites and hybrid developments, with a growing emphasis on minimising ecological impacts, optimising land use and reducing grid integration costs. By marrying rigorous spatial analysis with decision support tools, geospatial methods inform evidence-based policy, facilitate investor confidence and promote equitable expansion of clean energy infrastructure worldwide.

Research from Nature Portfolio

Recent studies have applied GIS and intuitionistic fuzzy-set theory to large-scale solar site selection, demonstrating consistency between geographic suitability and advanced decision-making outputs. One investigation of a province in Turkey combined ten ecological and technical criteria—such as land surface temperature, transmission line proximity and land use—within a GIS environment to generate a suitability map. An intuitionistic fuzzy approach assigned membership, non-membership and hesitation values to each criterion, enabling a nuanced aggregation. The study validated the overlap of high-suitability zones between the GIS overlay method and the fuzzy decision model, underscoring the robustness of hybrid multi-criteria frameworks for renewable energy planning.

Geospatial Analysis for Renewable Energy Site Selection publication trend

The graph below shows the total number of articles in geospatial analysis for renewable energy site selection across all publications each year (not limited to Nature Index journals).

Technical terms

Geographic Information System (GIS): A computer system for capturing, storing, analysing and visualising spatial data in layered formats.

Remote Sensing: The acquisition of information about Earth’s surface via satellite or aerial sensors to derive environmental and resource variables.

Multi-Criteria Decision-Making (MCDM): A suite of methods to evaluate and rank alternatives based on multiple, often conflicting, criteria.

Analytic Hierarchy Process (AHP): A structured technique that uses pairwise comparisons to calculate weights for decision criteria and prioritise options.

Intuitionistic Fuzzy Set: An extension of fuzzy logic that captures degrees of membership, non-membership and uncertainty for each criterion.

Digital Elevation Model (DEM): A digital representation of terrain elevation used to determine slope, aspect and hydrological characteristics.

Suitability Index: A composite score derived from combining weighted criteria to indicate the appropriateness of a site for development.

References

1. GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues. ISPRS International Journal of Geo-Information (2014).
2. Multi-criteria decision making for solar power - Wind power plant site selection using a GIS-intuitionistic fuzzy-based approach with an application in the Netherlands. Energy Strategy Reviews (2024).
3. Multi-criteria of PV solar site selection problem using GIS-intuitionistic fuzzy based approach in Erzurum province/Turkey. Scientific Reports (2021).
4. A Two-Stage Multiple Criteria Decision Making for Site Selection of Solar Photovoltaic (PV) Power Plant: A Case Study in Taiwan. IEEE Access (2021).