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Found 11 papers, 4 papers with code
CityTFT: Temporal Fusion Transformer for Urban Building Energy Modeling
Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems against the increasing energy demand at urban and neighborhood levels.
Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation
Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data.
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn
To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response.
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
Building upon prior research that highlighted the need for standardizing environments for building control research, and inspired by recently introduced challenges for real life reinforcement learning control, here we propose a non-exhaustive set of nine real world challenges for reinforcement learning control in grid-interactive buildings.
Model Predictive Control
Multi-agent Reinforcement Learning
+2
MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering
HVAC systems account for a significant portion of building energy use.
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management
Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network.
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management
Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid.
The ASHRAE Great Energy Predictor III competition: Overview and results
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
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
Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA).
