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).
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Updated
Nov 21, 2023 - Jupyter Notebook
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).
Predicting the Energy consumed by appliances using Machine Learning algorithms built from scratch
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.
Time Series Forcasting and Clustering for Energy Management - Machine Learning & Imputation
What is the SOTA technique for forecasting day-ahead and intraday market prices for electricity in Germany?
In this section, predicting the energy efficiency of buildings with machine learning algorithms.
Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668
list of papers, code, and other resources
Predicted Burned area of forest fires and Turbine yield energy using ANN
Experimental data used to create regression models of appliances energy use in a low energy building.
My solution to solve the second IEEE-CIS technical challenge
Paper in Science and Technology for the Built Environment about the GEPIII Competition
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