Big IoT data mining for real-time energy disaggregation in buildings

By Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta. 2016

42 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Wei Yu
4. Mohsen Guizani
5. Sameh Sorour
6. Ekram Hossain
7. Syed Ali Hassan
8. Rasheed Hussain

In Electrical and Electronic Engineering

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Wei Yu
4. Mohsen Guizani
5. Sameh Sorour
6. William Grant Hatcher

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[121] have developed a kind of RBM to identify and predict the buildings’ energy flexibility”
• A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, methodology): “[94] utilized Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs) and Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines (DFFW-CRBMs)”
• A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, methodology): “In addition, the existing research efforts have demonstrated the application of machine learning techniques for I-IoT systems [148]–[152].”