107 citations · 152 across the 3 of their papers we have counts for
5 papers
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management
Aisling Pigott, Constance Crozier, Kyri Baker +1
Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network. Intelligent control o…
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management
Jose R Vazquez-Canteli, Sourav Dey, Gregor Henze +1
Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid. In the US,…
The ASHRAE Great Energy Predictor III competition: Overview and results
Clayton Miller, Pandarasamy Arjunan, Anjukan Kathirgamanathan +8
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition…
The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition
Clayton Miller, Anjukan Kathirgamanathan, Bianca Picchetti +7
This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 meas…
Quick-cast: A method for fast and precise scalable production of fluid-driven elastomeric soft actuators
Bratislav Svetozarevic, Moritz Begle, Stefan Caranovic +2
Fluid-driven elastomeric actuators (FEAs) are among the most popular actuators in the emerging field of soft robotics. Intrinsically compliant, with continuum of motion, large stro…