list of papers, code, and other resources

What is the SOTA technique for forecasting day-ahead and intraday market prices for electricity in Germany?

A project focused on forecasting solar photovoltaic (PV) power generation using regional microclimate data. Implements machine learning models like CatBoost, LightGBM, and XGBoost for predictions, leveraging environmental features like temperature, humidity, wind speed, and solar radiation.

Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of defects in 2D materials. npj Comput Mater 9, 113 (2023).

In this section, predicting the energy efficiency of buildings with machine learning algorithms.

Paper in Science and Technology for the Built Environment about the GEPIII Competition

ConfRank+: Extending Conformer Ranking to Charged Molecules

My solution to solve the second IEEE-CIS technical challenge

Prediction of turbine energy yield (TEY) using Neural Networks

Machine Learning Project on Electricity Consumption For Household Appliances. Random Forest gave us the best results. Model achieved 97% accuracy to optimize appliance‑level energy usage and reduce costs.

Predicting the Energy consumed by appliances using Machine Learning algorithms built from scratch

A Flask-based web application to forecast wind turbine renewable energy generation using time-series feature engineering and a pre-trained XGBoost model. Users can input custom date ranges and visualize future energy predictions through dynamic Matplotlib plots.

Pipeline complet de prédiction de consommation électrique. Multiples modèles (ML, Deep Learning, séries temporelles) avec interface Streamlit et optimisation d'hyperparamètres.

Experimental data used to create regression models of appliances energy use in a low energy building.

Time Series Forcasting and Clustering for Energy Management - Machine Learning & Imputation

Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668

SynapticGrid is an AI-driven system designed to make cities more efficient, sustainable, and livable by optimizing smart energy grids, waste management, and traffic flow through IoT sensors, real-time data processing, and reinforcement learning algorithms. The modular platform continuously learns and improves, helping urban environments

Wind Power Generation Forecasting using Machine Learning A data-driven forecasting project that uses machine learning models to predict wind power generation based on historical energy and weather data. Includes preprocessing, EDA, model training, and performance evaluation—all inside a well-documented Jupyter Notebook.

Predicting electricity demand using LSTM and Random Forest models. A Comparative study with load & weather data

This project explores two different investigation using methods of machine learning and hybrid approach to predict the peak energy consumption in Ireland and energy production in Portugal based on weather and time variables by evaluating performance for classification metrics and regression metrics.