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

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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 multi-agent stochastic simulation (MASS) platform has been shown to be an effective tool in training model predictive control (MPC) algorithms, and in general to reduce the uncertainty in energy simulations of building performance. The MASS platform is openly available for academic and non-academic beneficiaries. The outcomes of the MPC research are also of value to non-academic beneficiaries.
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.