This dataset is used for training of component based machine learning (CBML) models described in the article. The article examines the effect of increasing and enriching training data on machine learning model's ability to generalise. Please read the full article for the relevant details of ML models. There are seven training dataset BaseCase, E-1, E-2, E-3, I-1, I-2, and I-3 and one test dataset. The trained machine learning (ML) components are saved under 'Models' folder in each dataset
Machine learning (ML) can be a valuable tool for discovering opportunities to save energy and resour...
Supporting information for the paper "Machine Learning Prediction of Critical Cooling Rate for Metal...
Machine learning (ML) methods has recently contributed very well in the advancement of the predictio...
This dataset is used for training of component based machine learning models described in the linked...
This dataset is used for training of deep learning (DL) component based machine learning models desc...
This dataset is used to verify machine learning (ML) energy predictions using the EnergyPlus (EP) si...
This dataset supports the article entitled "Machine Learning for Run-Time Energy Optimisation i...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
Machine learning (ML) models have been widely used in diverse applications of energy systems such as...
Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to ...
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy ...
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
This disclosure describes techniques to predict power consumption of a computing device under design...
Building energy predictions are playing an important role in steering the design towards the require...
Machine learning (ML) can be a valuable tool for discovering opportunities to save energy and resour...
Supporting information for the paper "Machine Learning Prediction of Critical Cooling Rate for Metal...
Machine learning (ML) methods has recently contributed very well in the advancement of the predictio...
This dataset is used for training of component based machine learning models described in the linked...
This dataset is used for training of deep learning (DL) component based machine learning models desc...
This dataset is used to verify machine learning (ML) energy predictions using the EnergyPlus (EP) si...
This dataset supports the article entitled "Machine Learning for Run-Time Energy Optimisation i...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
Machine learning (ML) models have been widely used in diverse applications of energy systems such as...
Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to ...
Machine learning (ML), a computational self-learning platform, is expected to be applied in a variet...
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy ...
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
This disclosure describes techniques to predict power consumption of a computing device under design...
Building energy predictions are playing an important role in steering the design towards the require...
Machine learning (ML) can be a valuable tool for discovering opportunities to save energy and resour...
Supporting information for the paper "Machine Learning Prediction of Critical Cooling Rate for Metal...
Machine learning (ML) methods has recently contributed very well in the advancement of the predictio...