Building energy prediction and management has become increasingly import...
The building sector plays a crucial role in the worldwide decarbonizatio...
Hybrid working strategies have become, and will continue to be, the norm...
The paper describes a dataset that was collected by infrared thermograph...
This work presents a study on the characterization of the air-conditioni...
People spend the majority of their time indoors and environmental condit...
This paper studies heat fluxes from contributors and mitigators of urban...
Before 2020, the way occupants utilized the built environment had been
c...
This paper describes the adaptation of an open-source ecological momenta...
We introduce Cohort Comfort Models, a new framework for predicting how n...
Data-driven building energy prediction is an integral part of the proces...
Collecting intensive longitudinal thermal preference data from building
...
The ASHRAE Great Energy Predictor III (GEPIII) competition was held in l...
In recent years, the availability of larger amounts of energy data and
a...
Conventional thermal preference prediction in buildings has limitations ...
Machine learning for building energy prediction has exploded in populari...
Thermal comfort assessment for the built environment has become more
ava...
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) mach...
Evaluating and optimising human comfort within the built environment is
...
In this paper, we represent a methodology of a graph embeddings algorith...
The activity-based workspace (ABW) paradigm is becoming more popular in
...
This paper describes an open data set of 3,053 energy meters from 1,636
...
This paper describes an open data set of 3,053 energy meters from 1,636
...
Building energy performance benchmarking has been adopted widely in the ...