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

In developed countries, buildings are involved in almost 50% of total energy use and 30% of
global green-house gas emissions. Buildings' operational energy is highly dependent on …

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 efficient and accurate prediction of building energy consumption can improve the
management of power systems. In this paper, the rough set theory was used to reduce the …

This study compares building electric energy prediction approaches that use a traditional
statistical method (linear regression) and artificial neural network (ANN) algorithms. We …

Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM)
operation can significantly degrade both the excavation and safety of tunnel construction. In …

Due to industrialization and the rising demand for energy, global energy consumption has
been rapidly increasing. Recent studies show that the biggest portion of energy is consumed …

Building energy consumption prediction plays an important role in improving the energy
utilization rate through helping building managers to make better decisions. However, as a …

Water is the most necessary and significant element for all life on earth. Unfortunately, the
quality of the water resources is constantly declining as a result of population development …

During an era of rapid growth in electricity demand throughout society, accurate forecasting
of electricity loads has become increasingly important to guarantee a stable power supply …