Building Energy Models at Different Time Scales Based on Multi-Output Machine Learning

Calibrating building energy models using supercomputer trained machine learning agents

Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This ...

Building Agent-Based Walking Models by Machine-Learning on Diverse Databases of Space-Time Trajectory Samples

We introduce a novel scheme for automatically deriving synthetic walking (locomotion) and movement (steering and avoidance) behavior in simulation from simple trajectory samples. We use a combination of observed and recorded real-world movement trajectory samples in conjunction with synthetic, agent-generated, movement as inputs to a machine-learning scheme. This scheme produces movement behavi...

Comparative Study on Machine Learning for Urban Building Energy Analysis

There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age gr...

Soil Landscape Models at Different Scales Portrayed

Performance of AERMOD at different time scales

As high-density monitoring networks observing pollutant concentrations are costly to establish and maintain, researchers often employ various models to estimate concentrations of air pollutants. The AMS/EPA Regulatory Model (AERMOD) is a fairly recent and promising model for estimating concentrations of air pollutants, but the effectiveness of this model at different time scales remains to be v...