Development of a feasibility prediction tool for solar power plant installation analyses

The solar energy becomes a challenging area among other renewable sources since the solar energy sources have the advantages of not causing pollution, having low maintenance cost, and not producing noise due to the absence of the moving parts. Although these advantages, the installation cost of a solar power plant is considerably high. However, feasibility analyses have a great role before installation in order to determine the most appropriate power plant site. Despite there are many methods used in feasibility analysis, this paper is focused on a new intelligent method based on an agglomerative hierarchical clustering approach. The solar irradiation and insolation parameters of Central Anatolian Region of Turkey are evaluated utilizing the intelligent feasibility analysis tool developed in this study. The clustering operation in the tool is performed by using the nearest neighbor algorithm. At the stage of determining the optimum hierarchical clustering results, Euclidean, Manhattan and Minkowski distance metrics are adapted to the tool. The achieved clustering results based on Minkowski distance metric provide the most feasible inferences to knowledge domain expert according to other distance metrics.

► An agglomerative hierarchical clustering tool is designed for renewable energy sources in this study.
► In the model, nearest neighbor approach is used as clustering algorithm and Euclidean, Manhattan, and Minkowski distance metrics as distance equations.
► The developed tool assists knowledge domain expert in terms of analysing extensive datasets.
► The developed tool clusters the given sample data efficiently and successfully using each distance metrics.
► The clustering results are compared according to success rates.