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NEW PAPER: Calibration Of High-Fidelity Building Energy Models To Support Model-Predictive Control (MPC) Of HVAC Systems

By Edwina Cramp on Wednesday 9 November 2016

At this year’s IBPSA-England Building Simulation and Optimisation (BSO) Conference in Newcastle 12-14th September, Marie Curie Fellow for our EINSTEIN R&D project Daniel Coakley, and IES’ Senior Software Leader Gordon Aird who has been working on the Energy in Time R&D project presentated a paper on the topic “Application Of An Optimisation Approach For The Calibration Of High-Fidelity Building Energy Models To Support Model-Predictive Control (MPC) Of HVAC Systems”.

Heating, ventilation and air-conditioning (HVAC) accounts for up to 50% of building energy consumption, and studies have shown significant potential for savings through the utilisation of fault detection and smart predictive control in place of traditional reactive based control systems. This paper  proposes a strategy for implementing intelligent model-predictive control (MPC) of HVAC systems based on calibrated high-fidelity models and real-time performance data.

It also includes a case study showing the application of the proposed approach on the development and calibration of a Building Energy Model (BEM) for a 2,775m2 commercial building in Helsinki, Finland.

The full paper presented can be read here:
aird-coakley-kerrigan—2016—application-of-an-optimisation-approach….pdf


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