Clustering Solar Energy Production

Project Overview

An unsupervised clustering application designed to identify clean energy grid zones. Segmenting geographical locations into cohesive solar production regions based on longitudinal yields.

Development Process

Applied Principal Component Analysis (PCA) to reduce solar yield dimensionality. Used K-Means clustering algorithm, optimizing the number of clusters using Elbow Method and Silhouette Coefficient. Visualized spatial cluster segments on map charts.

Key Features

• Unsupervised learning grouping geographical areas by solar output patterns
• K-Means clustering algorithm optimized using Silhouette Analysis and Elbow graphs
• Dimensionality reduction via PCA for multi-spectral meteorological attributes
• Seaborn data visualization layers mapping regional solar clusters
• Comparative profiling of each cluster's average yield potential

Challenges & Solutions

Determining the optimal number of clusters without prior labeling. Solved by calculating Silhouette Coefficients alongside Inertia scores across various cluster parameters to identify the best elbow point.

Technologies Used