Solar Power Generation Forecasting Engine
Predictive regression modeling and exploratory data science pipeline analyzing multi-sensor weather & irradiation telemetry
AI/ML Engineer & GenAI Systems Architect • Savitribai Phule Pune University (SPPU)
The Problem Statement
Solution Architecture & Implementation
Developed during the Data Science Internship at Altair Engineering. The pipeline ingests multi-sensor historical telemetry from solar plant inverters and weather monitoring stations. Conducted statistical Exploratory Data Analysis (EDA), Pearson correlation mapping, and feature transformations (polynomial irradiation terms). Built multiple regression models comparing Linear, Ridge, Lasso, and Random Forest algorithms before packaging the top-performing model into an interactive Streamlit dashboard.
Key Engineering Innovations
Covariance Feature Modeling: Discovered non-linear inverse efficiency thresholds where extreme module temperatures degrade solar output despite high solar irradiation.
Interactive What-If Dashboard: Empowered plant operators to simulate expected megawatt generation by manipulating expected cloud-cover percentages.
Robust K-Fold Validation: Applied time-aware train-test splits to avoid temporal data leakage across seasonal transitions.
Performance Benchmarks & Empirical Impact
| Metric | Before / Baseline | Engineered | Net Impact |
|---|---|---|---|
| Prediction R² Score | 0.78 (Standard Linear) | 0.884 (Optimized) | +10.4% Explanatory Power |
| Root Mean Squared Error (RMSE) | 48.2 kW | 32.1 kW | 33.4% Error Reduction |
| Inference Speed | Batch Scripts | <12ms per Prediction | Real-Time Telemetry Ready |
Technology Stack Architecture
Data Science & Stats
Machine Learning
Visualization & Apps
Looking for an AI/ML Engineer or GenAI Consultant?
Yash Jondhale delivers production-ready RAG pipelines, FastAPI microservices, and computer vision systems. Available for machine learning roles and enterprise AI advisory.