list of papers, code, and other resources

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

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

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.

My solution to solve the second IEEE-CIS technical challenge

Prediction of turbine energy yield (TEY) using Neural Networks

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

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

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

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

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.

This project is to develop a robust model capable of accurately predicting energy consumption in buildings. This endeavor involves harnessing historical energy usage data in conjunction with diverse weather and environmental variables to construct an effective predictive model.

Predicted Burned area of forest fires and Turbine yield energy using ANN

ML backend powering an energy consumption prediction dashboard.