Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Building

no code implementations • 25 Apr 2021 • Anjukan Kathirgamanathan, Eleni Mangina, Donal P. Finn

The SAC DRL was found to learn an optimal control policy that was able to minimise energy costs by 9. 7% compared to the default rule-based control (RBC) scheme and was able to improve or maintain thermal comfort limits over a test period of one week.

A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn

1 code implementation • 22 Sep 2020 • Anjukan Kathirgamanathan, Kacper Twardowski, Eleni Mangina, Donal Finn

Reinforcement learning is a promising model-free and adaptive controller for demand side management, as part of the future smart grid, at the district level.

Management reinforcement-learning +1

The ASHRAE Great Energy Predictor III competition: Overview and results

3 code implementations • 14 Jul 2020 • Clayton Miller, Pandarasamy Arjunan, Anjukan Kathirgamanathan, Chun Fu, Jonathan Roth, June Young Park, Chris Balbach, Krishnan Gowri, Zoltan Nagy, Anthony Fontanini, Jeff Haberl

This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s.

Computers and Society

The Building Data Genome Project 2: Hourly energy meter data from the ASHRAE Great Energy Predictor III competition

2 code implementations • 3 Jun 2020 • Clayton Miller, Anjukan Kathirgamanathan, Bianca Picchetti, Pandarasamy Arjunan, June Young Park, Zoltan Nagy, Paul Raftery, Brodie W. Hobson, Zixiao Shi, Forrest Meggers

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 measurements per meter resulting in approximately 53. 6 million measurements).

Applications