Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit
about one-third of greenhouse gases. In the last few years, machine learning has achieved …

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …

US residential buildings account for a significant share of national energy consumption,
highlighting their potential for energy-savings. Accurately predicting building energy …

Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …

The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …

Transfer learning can use the knowledge learned from the operating data of other buildings
to facilitate the energy prediction of a target building. However, most of the current research …

In recent years, water quality monitoring has been crucial to improve water resource
protection and management. Under the relevant laws and regulations, environmental …

Data are naturally collected in their raw state and must undergo a series of preprocessing
steps to obtain data in their input state for Artificial Intelligence (AI) and other applications …

Water quality is substantially influenced by a multitude of dynamic and interrelated variables,
including climate conditions, landuse and seasonal changes. Deep learning models have …

Building energy prediction and management has become increasingly important in recent
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …