Unlocking the Potential of Model-Predictive Control in Non-domestic Building Energy Management: Automated Configuration and Optimisation of Control

Lead Research Organisation: University of Sheffield
Department Name: Architectural Studies

Abstract

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Publications

10 25 50
 
Description We have demonstrated, through this award, that multi-agent stochastic simulation (MASS) combined with energy simulation is an effective tool in the training of model predictive control algorithms.
Exploitation Route Our MASS platform is described in journal papers and is openly available for use by others via a dedicated GitHub respository.
Sectors Construction

Digital/Communication/Information Technologies (including Software)

Energy

 
Description Our work in this project supported the generation of synthetic (deterministic and stochastic) data for the training model predictive control strategies. These strategies were subsequently investigated with a view to embedding in outcomes in the control of real building use cases. This knowledge was exchange directly with engineering consultants.
First Year Of Impact 2019
Sector Construction,Digital/Communication/Information Technologies (including Software),Energy
Impact Types Economic

 
Title Multi-Agent Stochastic Simulation platform 
Description A prototype Multi-Agent Stochastic Simulation (MASS) platform has been developed. This is being deployed to generate data to help train the Model Predictive Control algorithms that are being developed within this project. This MASS platform will shortly be disseminated under open source licence agreement. 
Type Of Technology Software 
Year Produced 2017 
Impact This is the most sophisticated platform for simulated occupants' stochastic behaviours and their energetic impacts; this latter through co-simulation with a detailed building simulation programme called EnergyPlus.