Multi-zone Building Research Articles

Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control—Application to a Residential Building

Climate change has made energy management a global priority. In France, the Grenelle Environment has set very ambitious progress targets for positive-energy buildings, particularly in terms of reducing and managing energy consumption. However, effective energy management in multi-zone buildings presents significant challenges, particularly when considering the inter-zone dynamics and heat transfer. This study examines multi-zone heating control, using a data-driven model for predictive indoor temperature modeling in intelligent buildings taking into account the influence of interconnected adjacent zones. The research methodology uses dynamic thermal simulation, parallel predictive models based on multiple linear regressions, and a multi-objective non-dominated sorting genetic algorithm II (NSGA-II) for the optimization process, which evaluates various generated heating strategies. This research introduces an approach to improve building energy efficiency by considering inter-zone dynamics and reducing heating-related energy consumption compared to a conventional heating strategy. By applying this model predictive control on a simulated case, a reduction in energy consumption due to heating is observed while respecting thermal comfort. This work contributes by implementing a method that independently controls temperatures in different building zones simultaneously while applying distinct constraints to each zone. This approach empowers occupants to manage heating consumption based on their preferences, ensuring personalized comfort. In addition, a comparison was made using a model that did not account for inter-zone interactions. This comparison demonstrates that incorporating these interactions into the predictive model enhances the effectiveness of the model predictive control approach. The multi-zone approach was also validated experimentally by using real experimental data, demonstrating significant reductions in energy consumption.

Pareto-Optimized Thermal Control of Multi-Zone Buildings Using Limited Sensor Measurements

Pareto-Optimized Thermal Control of Multi-Zone Buildings Using Limited Sensor Measurements

Decentralized Control Design for Heating System in Multi-Zone Buildings Based on Whale Optimization Algorithm

Decentralized Control Design for Heating System in Multi-Zone Buildings Based on Whale Optimization Algorithm

Multi-source transfer learning method for enhancing the deployment of deep reinforcement learning in multi-zone building HVAC control

Deep reinforcement learning (DRL) control methods have shown great potential for optimal HVAC control, but they require significant time and data to learn effective policies. By employing transfer learning (TL) with pre-trained models, the need to learn the data from scratch is avoided, saving time and resources. However, there are two main critical issues with this approach: the inappropriate selection of the source domain resulting in worse control performance and inefficient utilization of multi-source domain control experience. To address these challenges, a multi-source transfer learning and deep reinforcement learning (MTL-DRL) integrated framework is proposed for efficient HVAC system control. In order to select appropriate source domains, the contribution of various source domains to the target task is quantified first, followed by a comprehensive evaluation of transfer performance based on average energy consumption and average temperature deviation. The well-pretrained DRL parameters from the optimal multi-source transfer set are then sequentially transferred to the target DRL controller. Results from a series of transfer experiments between buildings with different thermal zones and weather conditions indicate that the MTL-DRL framework significantly reduces the training time of HVAC control, with improvements of up to 20% compared to DRL baseline models trained from scratch. Additionally, the MTL-DRL method leads to reductions in average energy consumption ranging from 1.43% to 3.12% and average temperature deviation up to 14.32%. The impact of the source domain transfer sequence on the performance of the DRL-based control method is also discussed. Overall, the proposed framework presents a promising solution for enhancing DRL-based HVAC control methods by reducing training time and energy consumption while maintaining occupants’ comfort.

Leveraging multi-zone building data with machine learning-based models and genetic algorithms to optimize air handling units

Optimizing air handling units (AHUs) in multi-zone buildings presents significant challenges. As buildings become more complex, the demand for tailored temperature control across various zones becomes paramount. This study leverages multi-zone building data with machine learning-based models and optimization techniques to optimize AHUs by specifically integrating deep neural network (DNN) modeling, clustering analysis, and a genetic algorithm (GA). We developed 18 models to predict AHU supply air temperature (Tsa) and 2 models to predict chiller power consumption (P) using DNN. Clustering analysis was applied to the return air temperature (Tra) of each AHU, serving as initial conditions for the GA. The clustering analysis provided insights into the AHU operational behavior, enhancing the optimization process precision. The GA's objective function maintained the AHUs' Tsa and minimized P by adjusting parameters such as valve openings, fan frequencies, chilled water supply temperature, and cooling water flowrate. The results indicated that DNN models demonstrated high predictive accuracy, with 16 models showing an R2 higher than 0.96. The results suggested that AHU411 requires increased capacity during high Tra condition, while AHU212 needs an enhanced model with more diverse training data or the incorporation of additional influencing factors. Validation revealed that this approach improved temperature maintenance across zones, with 96 % of the data falling within demand temperature range (Tset), and achieving Tset required 41 % higher power input.

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

A hybrid data assimilation method for reconstructing airflow path parameters of a multi-zone model

The indoor airborne pollutant has a great impact on indoor air quality. During the calculation process of pollutant distribution, some difficult-to-obtain parameters are critical in ensuring the calculation accuracy. In this study, we proposed a hybrid data assimilation method combined with Ensemble Kalman Filter (EnKF) and Gibbs sampling to reconstruct airflow path parameters (flow coefficient and flow index) in a multi-zone building model. Specially, we built a scaled multi-zone building model and used CO2 as the tracer gas to simulate the pollutant distribution in the building. The monitored sensor concentration data were used as prior information to reconstruct airflow path parameters of the scaled building model. And the performances of different data assimilation methods were compared. The results show that the EnKF method can reduce the calculation time by 46 % compared to the Gibbs sampling method, but the predicted accuracy decreases by 38 %. However, when we used the hybrid algorithm to reconstruct the airflow path parameters, the calculation time was reduced by 67 % compared to the EnKF method, and the predicted reconstruction accuracy is 7 % higher than the Gibbs sampling method.

Distributed model predictive control of fan coil system

To address the issues of large overshoot, long system stabilization time, and slow response associated with traditional PID-based and centralized model predictive control (MPC) of fan coil systems, this study proposes a distributed model predictive control (DMPC) method suitable for multi-zone building temperature regulation. Specifically, based on the thermal dynamic model of different zones, and aiming to constrain indoor temperature within set limits and minimize water system pressure drop and fan coil speed, a distributed MPC architecture for fan coil systems is developed. To solve the distributed optimization problem, the Nash equilibrium is introduced to find the global balance point within the distributed structure, and the Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) hybrid algorithm is used for online optimization of local MPCs. Experimental results show that compared to traditional PID control, the distributed MPC achieves coordinated multi-zone indoor temperature regulation with better dynamic performance, with the average absolute temperature error in each zone below 5 % and an energy savings rate of 16.23 %. Through semi-physical simulation, it is demonstrated that the proposed distributed MPC method has better applicability when the number of fan coils exceeds 10, compared to centralized MPC.

A Markov chain-based approach for assessing respiratory infection risk in a multi-zone office building

Most respiratory infectious disease transmission events occur in indoor environments, and examining the infection risk in different types of multi-zone buildings is critical to the safe reopening and use of public buildings. Appropriate space design influences occupant behavior and virus spread, thereby aiding in mitigating indoor infection risk. This study aims to quickly evaluate the multi-route infection risk in multi-zone buildings with relatively stable occupant populations, and to quantify the effectiveness of spatial interventions at different levels. A Markov chain-based approach was proposed to characterize occupants' time-activity trajectories with low time cost and relatively high accuracy. Four transmission routes are considered, especially for the fine-grained characterization of close contact. A real university office building was selected as a case study to quantitatively evaluate the effects of three non-pharmacological interventions including spatial layout, ventilation, and occupant behavioral control. Results showed that short-range airborne transmission dominated, and optimized combination spatial design interventions reduced the indoor infection risk by up to 10 %. Zone layout intervention was the most effective, with the division of the open-plan office area into three subzones, reducing the risk by 9.6 %, which was superior to the intervention of maintaining a one-meter interpersonal distance. Followed by the furniture layout, without changing the seating capacity and without desktop baffles, the L-shaped table layout worked better reducing the risk by 2.78 %. This study can help architects and managers better understand the risk of indoor transmission in multizone buildings from a spatial design perspective, to select appropriate interventions to reduce the risk.

Wind-induced ventilation rate of single-sided ventilation in a building with internal partition

Wind-induced single-sided ventilation is a prevalent form of natural ventilation extensively used in buildings. Nevertheless, prior experimental investigations predominantly focused on single-zone buildings, neglecting the multizone buildings with internal partitions which is representative of more common scenarios. This study addresses this gap by investigating the impact of internal partitions on single-sided ventilation, employing a combination of wind tunnel experiment and numerical analysis. Airflow rate (AFR) was measured with a split-fibre probe and purging flow rate (PFR) was assessed by the tracer gas methodology. The PFR exhibits greater sensitivity to internal partitions in unidirectional airflow compared to bidirectional flow. Large Eddy Simulation (LES) was conducted to elucidate the intricate airflow characteristics in single-sided ventilation. The ventilation efficiency (ratio of PFR and AFR) derived from LES ranges between 0.74 and 0.79, which means that <80% of the AFR actively contributes to the removal of contaminants. Notably, the investigation discerned that the AFR of a single room approximates that of the entire room, whereas the PFR of a single room is smaller than that of the whole room. The disparities in AFR and PFR were caused by the recirculating flow, which was elaborated by the theoretical analysis.

A Distributed ADMM-Based Deep Learning Approach for Thermal Control in Multi-Zone Buildings Under Demand Response Events

A Distributed ADMM-Based Deep Learning Approach for Thermal Control in Multi-Zone Buildings Under Demand Response Events

Distributed LSTM-GCN-Based Spatial–Temporal Indoor Temperature Prediction in Multizone Buildings

Distributed LSTM-GCN-Based Spatial–Temporal Indoor Temperature Prediction in Multizone Buildings

Development of an energy digital twin from a hotel supervision system using building energy modelling

In this work, the utilization of the supervision system of a five-story hotel located in Northern Italy as tool to calibrate and validate a building energy model (BEM) is evaluated. The BEM is created using the TRNSYS simulation environment with the aim to develop a digital twin, to study the energy consumption of the building-HVAC system. The procedure for the supervision system preparation and the data analysis procedure is presented. The digital twin is then used to compare two control strategies for the thermostats’ control during summer to increase the energy efficiency of the building-HVAC system, and to use the heat storage properties of the building envelope to shift and shave the peaks of power demands. Control rules that allow a better match between electrical energy demand and availability, developed, and tested using the BEM, are pursued with the final goal to prepare the inclusion of the hotel in a smart grid. The building is modelled using TRNSYS Type 56 as a multi-zone building, with 96 thermal zones while the building monitoring is performed during the summer season from June to October 2022.

Degradation-aware data-enabled predictive control of energy hubs

Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy systems with batteries can improve the energy use of a building, while posing the challenge of considering battery degradation during control operation. We demonstrate the performance of a data-enabled predictive control (DeePC) approach applied to a single multi-zone building and an energy hub comprising an electric heat pump and a battery. In a comparison with a standard rule-based controller, results demonstrate that the performance of DeePC is superior in terms of satisfaction of comfort constraints without increasing grid power consumption. Moreover, DeePC achieved two-fold decrease in battery degradation over one year, as compared to a rule-based controller.

Quantifying the impact of Covid-19 on the energy consumption in the low-income housing in Greater London

Covid-19 has caused great challenges to the energy sector, particularly in residential buildings with low-income households. This study investigates the impact of the confinement measures due to the Covid-19 outbreak on the energy demand of seven residential archetype buildings in Greater London. Three levels of confinement for occupant schedules are proposed and compared with the base case before Covid-19. The archetypes, their boundary conditions, and input parameters are set up according to statistics from English Housing Survey (EHS) sample data for low-income housing. The base case scenario (normal life without confinement measures) is validated against the measured data energy consumption from the National Energy Efficiency Data-Framework (NEED) statistics. The results show that electricity consumption is significantly lower than that for heating and hot water for all the archetypes. By comparing the base case scenario with the full Covid-19 lockdown scenario, the results indicate that heating and hot water consumption (kWh) for all the residential archetypes increases, on average, by 10%, and total electricity demand (kWh) increases by 13%. The study highlights the importance of introducing detailed occupancy profiles in multi-zone building energy simulation models during a pandemic that leads to a greater shift towards home working, which may increase the risk of fuel poverty in low-income housing.

Adaptive Robust Data-Driven Building Control via Bilevel Reformulation: An Experimental Result

Data-driven control approaches for the minimization of energy consumption of buildings have the potential to significantly reduce deployment costs and increase the uptake of advanced control in this sector. A number of recent approaches based on the application of Willems’ fundamental lemma for data-driven controller design from input/output measurements are very promising for deterministic linear time-invariant (LTI) systems. This article proposes a systematic way to handle unknown measurement noise and measurable process noise, and extends these data-driven control schemes to adaptive building control via a robust bilevel formulation, whose upper level ensures robustness and whose lower level guarantees prediction quality. Corresponding numerical improvements and an active excitation mechanism are proposed to enable a computationally efficient reliable operation. The efficacy of the proposed scheme is validated by a multizone building simulation and a real-world experiment on a single-zone conference building on the École Polytechnique Fédérale de Lausanne (EPFL) campus. The real-world experiment includes a 20-day nonstop test, where, without extra modeling effort, our proposed controller improves 18.4% energy efficiency against an industry-standard controller while also robustly ensuring occupant comfort.

A virtual sensor network for pressure distribution inside a multi-zone building based on spatial adjacency relationships and multivariate adaptive regression spline

Airflows within multi-zone buildings cause variations in environmental parameters both vertically and horizontally. Although it is difficult to monitor the airflows through existing sensors, pressure monitoring allows the identification of airflows, since airflows across zones are induced by pressure differences. This study proposes a data-driven method for establishing a virtual pressure sensor network within a multi-zone building. Three scenarios with various sample sizes are applied to evaluate their impacts on the analyzed results, and scenario 3 is identified as the most suitable database for creating a virtual sensor network. The single-sided or multi-sided spatial adjacency relationships are determined for each zone, and the MARS model is then applied to develop the relationships among the variables, with key physical sensors designated by the frequency analysis of independent variables. The final sensor network comprises four key physical sensors, thirteen direct virtual sensors, and five indirect virtual sensors. All virtual sensors can achieve excellent predicted results with high R2 values and low MAE and RMSE, with mean values of 0.997 Pa and 1.269 Pa, respectively. Notably, the difference in accuracy between direct and indirect virtual sensors is influenced by spatial adjacency relationships. This study demonstrates a reliable virtual sensor network for predicting pressure variations and provides a reference for the identification of airflows and the efficient adjustment of HVAC systems.

An integrated simulation of intermittent heating of multi-zone buildings by heat-pump heating systems with different terminal types

Intermittent heating has been considered to be an effective way to achieve energy savings compared with continuous heating. However, recent studies have found that actual energy savings from intermittent heating vary greatly, depending on actual building conditions, terminals and heating supply systems. For multi-zone buildings adopting heat pumps with specific terminals, a comprehensive analysis of energy savings resulting from intermittent heating still needs to be done. In the present study, an integrated dynamic model is developed by considering the interaction of thermal processes among building envelopes, terminals, and heat pumps. Its reliability is verified in field tests, and a better accuracy was achieved when considering the radiant heat ratio of terminals. On this basis, the model together with a source and terminal control strategy is applied to simulate the thermal behavior of heat pump heating systems for buildings under various conditions. The effect of different terminals and building envelopes on room temperature variation and energy utilization by intermittent heating is analyzed. The simulation results and practical projects indicate that heat pump power consumption under intermittent heating may be lower, close to or even higher than that under continuous heating. The factors that affect the efficient of intermittent heating, including building and system thermal inertia, heat pump operation COP, the radiant heat ratio of terminals, need to be systematically considered. Specifically, fan-coil heating systems could use 16.48% less energy, while radiant floor heating systems consume more energy under intermittent heating. Heat pumps under continuous heating are more efficient in buildings with radiative heating systems and heavy envelopes.

Air quality and comfort constrained energy efficient operation of multi-zone buildings

Maintaining indoor air quality (IAQ) through effective ventilation is essential for the well-being and productivity of building occupants. Control strategies aimed at improving the efficiency of heating, ventilation and air conditioning (HVAC) systems must jointly determine ventilation and heating and cooling processes. In this paper, we study the problem of minimizing the energy consumption of the HVAC system in a multi-zone building, while meeting thermal comfort and IAQ requirements. We first perform a steady state analysis of the zonal carbon dioxide (CO2) concentration and the temperature dynamics. The resulting expressions are convex in the zonal mass flow rates and zonal temperatures. Guided by the steady state solutions for meeting the thermal comfort constraints, we develop two control policies for improving the energy efficiency of building HVAC systems while jointly satisfying indoor temperature and IAQ constraints. We compare the performance of our proposed approaches with those of multiple baseline approaches which implement separate regimes for controlling zonal temperature and IAQ for a typical work-day in a multi-zone campus building. We have evaluated the performance of our proposed approaches under varying levels of flexibility in zonal temperatures. We have shown that zonal temperature flexibility can result in energy savings up to 32% (for the same control strategies) as compared to the case where no such flexibility is permitted. Our proposed approaches were seen to offer potential savings of nearly 29% compared to the baseline.

In-situ empirical validation of common indoor climate parameters in an inhabited multizone dwelling

Combined building indoor climate and energy simulation models only recently gained vast popularity and their application has been moving from the research community to a broader audience. Yet, in-situ empirical validation of this new generation of complex multi-purpose dynamic simulation models has lagged behind. Using a dynamic multizone building indoor climate and energy simulation model in Modelica with the IDEAS library and buoyancy driven airflow components (validated with CONTAM), this research presents model validation results and lessons learned from an in-situ empirical validation study of common indoor climate parameters (i.e., indoor air temperature (), relative humidity (RH) and CO2 concentration (CO2)) for an inhabited and mechanically ventilated case study dwelling in The Netherlands. The simulation results show that the latest generation of building indoor climate and energy models in Modelica have great ability to accurately predict common indoor climate parameters in multizone inhabited dwellings (provided that user behavior info is available). Evaluation metrics for the three studied parameters show excellent calibration criteria (i.e., MAE between 0.60–0.78 °C (), 3.5–4.6% (RH) and 88–181 ppm (CO2)) and the accompanying graphs corroborate the findings. In the event that no motion sensor data is available, statistically generated occupancy profiles prove good representative alternatives on the condition that basic info is available about the number of inhabitants and the inhabitants’ lifestyle. In-situ monitoring for empirical model validation proves to be a real challenge full of (un)foreseen obstacles.