Applied machine learning toolkit implementing Double Machine Learning for Energy Analytics.

Multi-AI Agent Energy Management System with HILS simulation, Hybrid AI (ML+LLM), and MCP Runtime - Real-time visualization demo for smart grid optimization

Repository of the paper "Detecting and interpreting faults in vulnerable power grids with machine learning".

Complete data architecture and machine learning project for industrial cost analysis. Includes a modern ETL pipeline (Bronze-Silver-Gold), validations with Great Expectations, Prefect orchestration, in-depth EDA, predictive models (Random Forest and Gradient Boosting), and deployment via Streamlit.

Data-driven analysis of Nigeria’s electricity system (2000–2024) using Python, Sql, Power BI.

AI system for energy demand forecasting and grid optimization. Uses time series analysis and weather data to predict energy consumption, optimize renewable energy integration, and prevent grid failures.

🔋⚡ Professional LOAD OUTPUT monitoring for Victron MPPT solar chargers - Real-time kWh tracking of load consumption with ESP82XX + ioBroker + MQTT

Data-driven analysis of solar farm data from Benin, Sierra Leone, and Togo, using statistical analysis and EDA to identify high-potential regions for solar installations. Delivers actionable insights to enhance operational efficiency and support sustainable energy investments.

Learn how to visualize power consumption trends using Syncfusion WPF Surface Charts. This guide covers creating interactive 3D charts, binding energy data, customizing chart appearance, and optimizing performance to display annual power usage patterns across countries.

Smart Grid ETL + Real-Time Energy Anomaly Analytics Dashboard

SPARK is a extensible energy analytics platform designed for processing large-scale renewable energy datasets using the Hadoop MapReduce framework. It offers data analytics, machine learning-based forecasting, and energy trend insights in a fully modular setup utilizing Hadoop, MapReduce, Apache Spark complementing the Streamlit UI.

AI-powered Energy Forecasting System using XGBoost, MLP & LSTM. Includes Flask API, responsive frontend, feature engineering pipeline, notebooks, datasets, and end-to-end machine learning workflow. Perfect for energy analytics, predictive modeling, and ML engineering portfolios.

Scenario-based analysis of global oil market supply–demand balances, OPEC+ policy sensitivity and geopolitical risk drivers, using OPEC, EIA and OEUK data.

Production-ready R/Shiny application for smart meter analytics featuring multi-algorithm anomaly detection (IQR, Z-Score, STL, Moving Average), k-means pattern clustering, and rate plan cost optimization with automated Excel reporting.

End-to-end time series forecasting project to predict power consumption using SARIMA, including data analysis, modeling, and an interactive user interface.

This repository contains my structured notes from DataCamp courses, exercises, and projects. It’s meant to serve as a quick reference for me and a showcase of my learning journey.

SPARK is a extensible energy analytics platform designed for processing large-scale renewable energy datasets using the Hadoop MapReduce framework. It offers data analytics, machine learning-based forecasting, and energy trend insights in a fully modular setup utilising Hadoop, MapReduce, Apache Spark complementing the Streamlit UI.

An end-to-end Data Science pipeline analyzing the impact of extreme heat on India's energy grid. Features automated NASA weather data processing, state-wise energy demand correlation, and a live Streamlit dashboard.

Seasonal time-series forecasting of power consumption using SARIMA, with full stationarity diagnostics, ACF/PACF analysis, and interpretable forecasts.

⚡ Projetos de Ciência de Dados para Transição Energética e Energia Renovável no Brasil