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.

Interface enabling use of ANI-style, and other NN-IPs in the Amber molecular dynamics software suite. Works with both Amber engines, sander and pmemd.

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).

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

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

My solution to solve the second IEEE-CIS technical challenge

ConfRank+: Extending Conformer Ranking to Charged Molecules

Prediction of turbine energy yield (TEY) using Neural Networks

⚡ AI-powered energy consumption prediction app. Flutter + FastAPI + ML. Reduce bills & carbon footprint . Smart energy optimization! 🌱

AI-powered hybrid energy prediction system using ML models (XGBoost, LSTM) with FastAPI backend and Streamlit dashboard for real-time solar & wind energy optimization.

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

Full-stack machine learning project for predicting building energy consumption using FastAPI, Next.js, and reproducible Jupyter notebooks.

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.

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.

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.

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

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

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