Occupancy schedules learning process through a data mining framework

By Simona D'Oca, Tianzhen Hong. 2015

298 citations recorded by the source corpus.

Subfields: Building and Construction, Renewable Energy, Sustainability and the Environment, Social Psychology

View Paper: https://www.semanticscholar.org/paper/81a5a531395378b8f79d9ffcb05b850b0a4fb2dc

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. Lynn H. Kaack
2. Felix Creutzig

In Renewable Energy, Sustainability and the Environment

1. Lynn H. Kaack
2. Felix Creutzig
3. Yi Yang

In Social Psychology

1. Yoshua Bengio
2. Natasha Jaques
3. Konrad Paul Kording
4. Demis Hassabis

Citations in the corpus

• Tackling Climate Change with Machine Learning (2019, background): “Moreover, occupancy detection itself represents an opportunity for ML algorithms, ranging from decision trees [267, 268] to deep neural networks [269] that take input from occupancy sensors [267], WiFi signals [269, 270], or appliance power consumption data [268].”
• Occupancy data at different spatial resolutions: Building energy performance and model calibration (2021, background): “[26] Sun K, Yan D, Hong T, Guo S. Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration.”