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 recent global warming effect has brought into focus different solutions for combating
climate change. The generation of climate-friendly renewable energy alternatives has been …

Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …

Abstract Machine learning has gained increasing popularity in building energy management
due to its powerful capability and flexibility in model development as well as the rich data …

With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …

To mitigate the impact of climate change and global warming, the use of renewable energies
is increasing day by day significantly. A considerable amount of electricity is generated from …

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 …

A recent surge of interest in building energy consumption has generated a tremendous
amount of energy data, which boosts the data-driven algorithms for broad application …

Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …

Building energy consumption prediction plays an irreplaceable role in energy planning,
management, and conservation. Data-driven approaches, such as artificial neural networks …