This dataset is used for training of deep learning (DL) component based machine learning models described in the linked article. The article examines the effect of enriching training data with several building shapes on the prediction accuracy of machine learning models. There are nine building shapes used to collect the training data using EnergyPlus. Please read the full article for the relevant details of component structure and training of DL components. There are seven training dataset BaseCase, E-1, E-2, E-3, I-1, I-2, and I-3 and one test dataset TestData. The trained DL component are saved under Models folder in each dataset. The performance.csv file inside each dataset folder describes the performance of DL components trained on th...
Unprecedented high volume of data is available with the upward growth of the advanced metering infra...
Unprecedented high volumes of data are available in the smart grid context, facilitated by the growt...
The consumption of energy in buildings has elicited the occurrence of many environmental problems su...
This dataset is used for training of component based machine learning models described in the linked...
This dataset is used for training of component based machine learning (CBML) models described in the...
Artificial Neural Networks (ANN) are a universal approximator for any non-linear function. However, ...
The reduction of energy consumption of buildings requires consideration in early design phases. Howe...
This dataset is used to verify machine learning (ML) energy predictions using the EnergyPlus (EP) si...
In this paper, deep learning methods are compared with traditional statistical learning approaches f...
In this paper the more advanced, in comparison with traditional machine learning approaches, deep le...
This dataset supports the article entitled "Machine Learning for Run-Time Energy Optimisation i...
With population increases and a vital need for energy, energy systems play an important and decisive...
International audienceThe training energy efficiency of deep neural networks became an extensively s...
Deep learning has produced some of the most accurate and most versatile techniques for many applicat...
<p>Energy consumption predictions for buildings play an important role in energy efficiency and sust...
Unprecedented high volume of data is available with the upward growth of the advanced metering infra...
Unprecedented high volumes of data are available in the smart grid context, facilitated by the growt...
The consumption of energy in buildings has elicited the occurrence of many environmental problems su...
This dataset is used for training of component based machine learning models described in the linked...
This dataset is used for training of component based machine learning (CBML) models described in the...
Artificial Neural Networks (ANN) are a universal approximator for any non-linear function. However, ...
The reduction of energy consumption of buildings requires consideration in early design phases. Howe...
This dataset is used to verify machine learning (ML) energy predictions using the EnergyPlus (EP) si...
In this paper, deep learning methods are compared with traditional statistical learning approaches f...
In this paper the more advanced, in comparison with traditional machine learning approaches, deep le...
This dataset supports the article entitled "Machine Learning for Run-Time Energy Optimisation i...
With population increases and a vital need for energy, energy systems play an important and decisive...
International audienceThe training energy efficiency of deep neural networks became an extensively s...
Deep learning has produced some of the most accurate and most versatile techniques for many applicat...
<p>Energy consumption predictions for buildings play an important role in energy efficiency and sust...
Unprecedented high volume of data is available with the upward growth of the advanced metering infra...
Unprecedented high volumes of data are available in the smart grid context, facilitated by the growt...
The consumption of energy in buildings has elicited the occurrence of many environmental problems su...