Julien Eynard

Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings

Graphical abstractDisplay Omitted HighlightsA model-based predictive approach is proposed to manage multizone HVAC systems.The predicted mean vote (PMV) index is used as a thermal comfort indicator.Low-order ANN-based models are developed and used as controllers internal models.The optimization problem is solved using a genetic algorithm.HVAC operation is optimized and thermal comfort constraints are met. In France, buildings account for a large part of the energy consumption and carbon emissions. Both are mainly due to heating, ventilation and air-conditioning (HVAC) systems. Because older, oversized or poorly maintained systems may be using more energy and costing more to operate than necessary, new management approaches are needed. In addition, energy efficiency can be improved in central heating and cooling systems by introducing zoned operation. So, the present work deals with the predictive control of multizone HVAC systems in non-residential buildings. First, a real non-residential building located in Perpignan (south of France) has been modelled using the EnergyPlus software. We used the predicted mean vote (PMV) index as a thermal comfort indicator and developed low-order ANN-based models to be used as controllers internal models. A genetic algorithm allowed the optimization problem to be solved. In order to appraise the proposed management strategy, it has been compared to basic scheduling techniques. Using the proposed strategy, the operation of all the HVAC subsystems is optimized by computing the right time to turn them on and off, in both heating and cooling modes. Energy consumption is minimized and thermal comfort requirements are met. So, the simulation results highlight the pertinence of a predicitive approach for multizone HVAC systems management.

Optimal Control of a Multi-Energy District Boiler: A Case Study

Abstract The present paper deals with the optimization of a multi-energy district boiler (La Rochelle, France) which supplies domestic hot water and heats residential and public buildings, using renewable and fossil resources. First, a combination of white, grey and black boxes was used to model the plant, thanks to a modular approach. Next, a stratified thermal storage tank was modelled and completed the just-mentioned plant model. Using these models and forecasted sequences about outdoor temperature and thermal power consumption, a model predictive controller allows optimizing the use of both the tank and the wood boiler. As a result, fossil energy consumption and CO 2 emissions are minimized. Energy is stored during low-demand periods and used when demand is high, instead of consuming gas and fuel oil.