The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …

The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

With the focus of smart construction project management, this paper presents a closed-loop
digital twin framework under the integration of Building Information Modeling (BIM), Internet …

In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …

With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …

Electric energy forecasting domain attracts researchers due to its key role in saving energy
resources, where mainstream existing models are based on Gradient Boosting Regression …

The vigorous expansion of renewable energy as a substitute for fossil energy is the
predominant route of action to achieve worldwide carbon neutrality. However, clean energy …

The rapid increase in human population and development in technology have sharply
raised power consumption in today's world. Since electricity is consumed simultaneously as …