Add to ORKGPrediction of building energy consumption is a fundamental problem in the smart grid context. Unprecedented high volumes of data and information are available with the upward growth of the smart metering infrastructure. Therefore, we develop two deep learning methods. Firstly, the demand forecasting problem was solved at low aggregation levels (i.e. 1900 buildings) using factored conditional restricted Boltzmann machine. Secondly, we developed an unsupervised energy prediction method using reinforcement cross-building transfer able to accurately estimate the energy based on the information available in the neighborhood. Both methods have been successfully validated on real-world databases
AbstractIn a future Smart Grid context, increasing challenges in managing the stochastic local energ...
The interconnection between the Smart Grid and Building Energy Management Systems involves complex i...
The electrical demand forecasting problem can be regarded as a non-linear time series prediction pro...
Prediction of building energy consumption is a fundamental problem in the smart grid context. Unprec...
In a future Smart Grid context, increasing challenges in managing the stochastic local energy supply...
To improve the design of the electricity infrastructure and the efficient deployment of distributed ...
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...
In the smart grid context, the identification and prediction of building energy flexibility is a cha...
In the smart grid context, the identification and prediction of building energy flexibility is a cha...
AbstractIn a future Smart Grid context, increasing challenges in managing the stochastic local energ...
The interconnection between the Smart Grid and Building Energy Management Systems involves complex i...
The electrical demand forecasting problem can be regarded as a non-linear time series prediction pro...
Prediction of building energy consumption is a fundamental problem in the smart grid context. Unprec...
In a future Smart Grid context, increasing challenges in managing the stochastic local energy supply...
To improve the design of the electricity infrastructure and the efficient deployment of distributed ...
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...
In the smart grid context, the identification and prediction of building energy flexibility is a cha...
In the smart grid context, the identification and prediction of building energy flexibility is a cha...
AbstractIn a future Smart Grid context, increasing challenges in managing the stochastic local energ...
The interconnection between the Smart Grid and Building Energy Management Systems involves complex i...
The electrical demand forecasting problem can be regarded as a non-linear time series prediction pro...