Evaluation and optimization of the performance of the heating system in a nZEB educational building by monitoring and simulation

Abstract

The optimal control of the HVAC system in the nearly zero energy buildings (nZEBs) can be considered as a challenge.Indeed, the lack of data given by monitored data does not allow understanding what are the implications of different strategies on thermal comfort and energy consumptions. This paper determines an approach for the evaluation of the management of the heating system in an existing educational nZEB. The aim is to understand how the energy efficiency of the heating system is sensitive to controls strategies and the important role played by the control systems. The test lecture rooms on KU Leuven Ghent Technology Campus (Belgium) have been chosen as case study.A deep analysis, starting from an in-field monitoring, was carried out during the winter period of 2019, in order to assess the actual operation and the energy use of building systems under real conditions. Then, a building energy simulation (BES) model of the building and its systems was developed and calibrated against the real monitoring data. Control strategies of the heating system are simulated and compared to the current configuration. The simulation results show that strategies based on a definition of a heating curve for the boiler can reduce the comfortable time (-23%). Controlling the temperature inside the storage tank as a function of the outdoor temperature together with the control on the activation of the air handling unit result in an energy savings from 32% to 46%, with a correspondent increase of comfort time in the period from 0.6% to 3.4% compared to the baseline scenario, depending to the relationship between the external temperature and water temperature inside the water storage tank.

Impact of heatwaves and system shocks on a nearly zero energy educational building: Is it resilient to overheating?

“…Temperatures were monitored at the room exhaust diffuser at 1-minute intervals using VAISALA GMW90 sensors [69] that measure temperatures between -5°C and 55°C with an accuracy of ±0.5°C. Apart from these measurements, continuous logging of the parameters like supply and return air temperatures, operation signal of the AHU, IEC, NNV, VAVs etc., was done using the BMS [70]. The Modelica model was validated under normal operating conditions without shocks (NS=base case) and a shock case (30-minute power outage) [68].…”

Optimal Control of Energy Systems in Net-Zero Energy Buildings Considering Dynamic Costs: A Case Study of Zero Carbon Building in Hong Kong

“…Ferrara et al [10] proposed a method based on deep residual learning, aimed at simplifying the nZEB optimization design, and the results showed that the use of this simulation method can effectively improve energy efficiency with low accuracy error. Borrelli et al [11] developed a building energy model for optimal control of Heating, Ventilation, and Air Conditioning (HVAC) systems in nZEB using a classroom located in Belgium as a case study. Experimental results showed that based on a combined outdoor time-by-time and thermal storage tank temperature control scheme, energy consumption can be significantly reduced (32-64%), and thermal comfort time was increased (0.6-3.4%).…”

Weather Files for the Calibration of Building Energy Models

“…However, several studies report that the results of simulation models in the design phase rarely match the measured performance, calling this deviation, the "energy performance gap" [20]. The possible causes are manifold, ranging from deviations between actual user loads and their predictions, different characteristics of HVAC systems, startups and shutdowns of HVAC systems [21], etc. Regardless of the error of the simulation software itself, the general solution proposed by the researchers to reduce this gap is to use as much real data as possible, from the building characteristics of its envelopes, accuracy when modeling spaces and the environment, and from outside conditions of the building, e.g., the climate [22,23].…”