[HTML][HTML] Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …
installed in residential buildings. If leveraged properly, that data could assist end-users …
Building energy prediction using artificial neural networks: A literature survey
C Lu, S Li, Z Lu - Energy and Buildings, 2022 - Elsevier
Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …
improving energy efficiency in building energy management systems. Essentially, building …
Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network
MN Fekri, H Patel, K Grolinger, V Sharma - Applied Energy, 2021 - Elsevier
Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …
importance for energy management, infrastructure planning, and budgeting. In recent years …
Edge-cloud computing for Internet of Things data analytics: Embedding intelligence in the edge with deep learning
AM Ghosh, K Grolinger - IEEE Transactions on Industrial …, 2020 - ieeexplore.ieee.org
Rapid growth in numbers of connected devices including sensors, mobile, wearable, and
other Internet of Things (IoT) devices, is creating an explosion of data that are moving across …
other Internet of Things (IoT) devices, is creating an explosion of data that are moving across …
Deep learning for the industrial internet of things (iiot): A comprehensive survey of techniques, implementation frameworks, potential applications, and future directions
The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast
communication protocols, and efficient cybersecurity mechanisms to improve industrial …
communication protocols, and efficient cybersecurity mechanisms to improve industrial …
A review and reflection on open datasets of city-level building energy use and their applications
Data related to building energy use fuels the research and applications on building energy
efficiency, which is an essential measure to address global energy and environmental …
efficiency, which is an essential measure to address global energy and environmental …
Design of concrete incorporating microencapsulated phase change materials for clean energy: A ternary machine learning approach based on generative adversarial …
The inclusion of microencapsulated phase change materials (MPCM) in construction
materials is a promising solution for increasing the energy efficiency of buildings and …
materials is a promising solution for increasing the energy efficiency of buildings and …
Predicting ultra-high-performance concrete compressive strength using tabular generative adversarial networks
There have been abundant experimental studies exploring ultra-high-performance concrete
(UHPC) in recent years. However, the relationships between the engineering properties of …
(UHPC) in recent years. However, the relationships between the engineering properties of …
A state-of-art-review on machine-learning based methods for PV
In the current era, Artificial Intelligence (AI) is becoming increasingly pervasive with
applications in several applicative fields effectively changing our daily life. In this scenario …
applications in several applicative fields effectively changing our daily life. In this scenario …
[HTML][HTML] Building power consumption datasets: Survey, taxonomy and future directions
In the last decade, extended efforts have been poured into energy efficiency. Several energy
consumption datasets were henceforth published, with each dataset varying in properties …
consumption datasets were henceforth published, with each dataset varying in properties …