Computer Engineer with a passion for data, renewable energy and intelligent systems.

I recently completed my BSc thesis — Machine Learning and Renewable Energy Communities: Models for Forecasting and Intelligent Management — combining hourly demand forecasting, shared-energy optimisation and Demand Response simulation within a real Renewable Energy Community (CER) in Benevento, Italy.

I bring 15+ years of practical experience in the Italian solar PV sector into machine learning and AI engineering, with a focus on energy and climate tech.

• CER-ML-Energy — Random Forest model for hourly energy-consumption forecasting and shared-energy optimisation in a Renewable Energy Community (CER) in Benevento
  • Temporal feature engineering with cyclic sin/cos encoding and lag features (t−1h, t−24h, t−168h)
  • PVGIS API integration for site-level PV production
  • Demand Response simulation with per-user load-shifting recommendations
  • Economic impact analysis on GSE incentives (Italian Decree D.M. 414/2023)
  • Results: R² 0.9935 · MAE 0.0262 kWh · MAPE 5.35% · +19% shared energy after DR

Python · scikit-learn · pandas · NumPy · Matplotlib · Google Colab

Also working with: Claude API · Claude Code (Anthropic certified, May 2026) for AI-assisted development and agent workflows.

AI engineering with LLMs and agents · ML for distributed energy systems · open to remote roles in climate / energy tech.

• Email: gioverlingieri@gmail.com
• Based in Benevento, Italy 🇮🇹