Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

Despite widespread adoption and outstanding performance, machine learning models are
considered as “black boxes”, since it is very difficult to understand how such models operate …

Accurate predictions of photovoltaic power generation (PV power) are essential for the
integration of renewable energy into grids, markets, and building energy management …

In recent decades, there has been a substantial rise in both worldwide energy consumption
and the accompanying increase in Carbon Dioxide (CO 2) emissions, primarily propelled by …

The energy sector plays an important role in socioeconomic and environmental
development. Accurately forecasting energy demand across various time horizons can yield …

In recent times, there has been a growing interest in urban building energy modeling
(UBEM), owing to its potential benefits for cities. These benefits include aiding city decision …

The intermittent nature of renewable resources like solar and wind presents challenges for
small-scale energy markets and off-grid regions. Localized forecasting of these resources is …

Redundant consumption of energy in buildings is an important issue that causes increasing
problems of climate change and global warming in the world. Therefore, it is necessary to …

Accurate forecasting of cooling and heating loads is critical for optimizing the energy usage
of devices and planning for energy storage in building district energy systems (BDESs). Data …

Due to the recent advancements in the Internet of Things and data science techniques, a
wide range of studies have investigated the use of data mining (DM) and machine learning …