Prediction of turbine energy yield (TEY) using Neural Networks
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Updated
Sep 18, 2024 - Jupyter Notebook
Prediction of turbine energy yield (TEY) using Neural Networks
Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668
Paper in Science and Technology for the Built Environment about the GEPIII Competition
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
In this section, predicting the energy efficiency of buildings with machine learning algorithms.
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
My solution to solve the second IEEE-CIS technical challenge
Predicted Burned area of forest fires and Turbine yield energy using ANN
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).
What is the SOTA technique for forecasting day-ahead and intraday market prices for electricity in Germany?
list of papers, code, and other resources
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