Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
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 …
about one-third of greenhouse gases. In the last few years, machine learning has achieved …
[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
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 …
improving grid stability and meeting service demand. This is possible by adopting next …
[HTML][HTML] Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
S Barja-Martinez, M Aragüés-Peñalba… - … and Sustainable Energy …, 2021 - Elsevier
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 …
systems by extracting value from the data generated by the deployed metering and sensing …
A comprehensive survey on imputation of missing data in internet of things
The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …
communication technologies, and Internet protocols with broad applications. Collecting data …
A transfer Learning-Based LSTM strategy for imputing Large-Scale consecutive missing data and its application in a water quality prediction system
Z Chen, H Xu, P Jiang, S Yu, G Lin, I Bychkov… - Journal of …, 2021 - Elsevier
In recent years, water quality monitoring has been crucial to improve water resource
protection and management. Under the relevant laws and regulations, environmental …
protection and management. Under the relevant laws and regulations, environmental …
[HTML][HTML] A taxonomy of machine learning applications for virtual power plants and home/building energy management systems
A Virtual power plant is defined as an information and communications technology system
with the following primary functionalities: enhancing renewable power generation …
with the following primary functionalities: enhancing renewable power generation …
[HTML][HTML] A combined deep learning application for short term load forecasting
An accurate prediction of buildings' load demand is one of the most important issues in
smart grid and smart building applications. In this way, an important contribution is made to …
smart grid and smart building applications. In this way, an important contribution is made to …
[HTML][HTML] A multi-source transfer learning model based on LSTM and domain adaptation for building energy prediction
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 …
to facilitate the energy prediction of a target building. However, most of the current research …
Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation
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 …
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …
An integrated missing-data tolerant model for probabilistic PV power generation forecasting
Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic
operation of a modern power system. In practice, due to various faulty issues in the sensor …
operation of a modern power system. In practice, due to various faulty issues in the sensor …