Multi-zone Building Research Articles

Empirical validation of a multizone building model coupled with an air flow network under complex realistic situations

Building energy simulation (BES) assesses the energetic and thermal behaviour of buildings and can be used for the evaluation of energy conservation measures (ECMs). However, building modelling under realistic conditions can be challenging for simulation programs. The aim of this work is to test whether a multizone building model developed with a simulation tool can handle the complex situations of an in-use building. Monitoring data from the Twin Houses empirical validation experiment of the International Energy Agency, Energy in Buildings and Communities (IEA EBC) Annex 71 is used for that purpose. The multizone building model is developed with the energy simulation software TRNSYS coupled with an air flow network in TRNFLOW. The innovative aspect of this work lies in the validation of a combined multizone building model under stochastic occupancy gains, taking into account room-wise heating profiles, ventilation, and interior air flow currents due to window and door operation. The results of a coheating test showed that the errors in the simulated heating power for the whole house are a coefficient of variation of the root mean square error (CV(RMSE)) of 12.72% and a normalised mean bias error (NMBE) of −8.35%. Nevertheless, more difficulties were found in separately predicting the heating power of each individual room. However, the combined multizone air flow model was able to predict indoor temperatures in all rooms with root mean square error (RMSE) and mean absolute error (MAE) values of<1 °C.

A wall solar chimney to ventilate multi-zone buildings

Although solar chimneys have been extensively investigated, their performance in multi-zone buildings have been overlooked previously. This study aims to examine the airflow characteristics inside a wall solar chimney and the attached ventilated multiple zones, together with the solar chimney performance, under various configurations (i.e., chimney cavity and room) and external environment (i.e., solar radiation intensity). The critical geometrical parameters of solar chimney and building layouts are then suggested based on the trade-offs between the buoyant flow patterns and the volumetric flow rate. Like those single zone applications, an increase in the chimney height and inlet location (until the middle height of the wall) results in a straight improvement in the airflow rate. By increasing the chimney height from 3.0 to 5.0 m, the ventilation capacity increases by about 90%; and moving the air inlet upward can get a 57.28% improvement of the ventilation rate. The maximum airflow rate in this study was observed for a solar chimney with a cavity gap of 0.2 m and an inlet size of 0.2 m, while enlarging the window area enhances the volume flow rate by up to 14.98%. In addition, when the cavity gap and inlet size exceed the optimal value, their influences on the airflow rate are observed insignificant, and the growth rate of volume flow rate decreases gradually. The multiple rooms with different aspect ratios seem to show a limited impact on the ventilation performance. The volume flow of four groups of room layouts shows a fluctuation of within 5%. The velocity distribution of the long and narrow room layout shows an average improvement of 2.5% than that of the two-room with a significant difference in width.

Simulation and validation of indoor temperatures and relative humidity in multi-zone buildings under occupancy conditions using multi-objective calibration

Buildings are expected to be energy efficient but also to provide a comfortable environment to their occupants as well as to be durable. Indoor relative humidity influences the energy consumption, structure and the occupants’ comfort and health. Therefore, it is necessary to control and predict indoor environmental conditions. This work aims to simulate indoor temperatures and relative humidity in a multi-zone building model under realistic conditions, such as occupancy and moisture gains, operation of windows and doors and mechanical ventilation. The combined multi-zone air flow model is developed with TRNSYS and TRNFLOW and it is applied to a phase of the Twin Houses extended experiment of the international energy agency, energy in buildings and communities (IEA EBC) Annex 71. The main novelty of this work is to determine the masses of surface and deep moisture buffer storage by performing a multi-objective calibration of indoor temperatures and relative humidity. Results prove that building energy simulations need to model the moisture buffering of internal materials to accurately predict indoor humidity. The error in simulating relative humidity is reduced by 69% in the house with moisture gains after calibration of the masses of buffering materials. Moreover, the calibrated multi-zone building model shows a great agreement between simulated and measured data with an average root mean square error (RMSE) among thermal zones of 0.51 °C and 0.48 °C in indoor temperatures and 3.58% and 2.21% in relative humidity for the two houses in the validation period.

Model Predictive Control-Based Hierarchical Control of a Multi-Zone Commercial HVAC System

Abstract This paper presents a novel architecture for model predictive control (MPC)-based indoor climate control of multi-zone buildings to provide energy efficiency. Unlike prior works, we do not assume the availability of a high-resolution multi-zone building model, which is challenging to obtain. Instead, the architecture uses a low-resolution model of the building that is divided into a small number of “meta-zones” that can be easily identified using existing data-driven modeling techniques. The proposed architecture is hierarchical. At the higher level, an MPC controller uses the low-resolution model to make decisions for the air handling unit (AHU) and the meta-zones. Since the meta-zones are fictitious, a lower level controller converts the high-level MPC decisions into commands for the individual zones by solving a projection problem that strikes a trade-off between two potentially conflicting goals: the AHU-level decisions made by the MPC are respected while the climate of the individual zones is maintained within the comfort bounds. The performance of the proposed controller is assessed via simulations in a high-fidelity simulation testbed and compared to that of a rule-based controller that is used in practice. Simulations in multiple weather conditions show the effectiveness of the proposed controller in terms of energy savings, climate control, and computational tractability.

Physics-constrained deep learning of multi-zone building thermal dynamics

We present a physics-constrained deep learning method to develop control-oriented models of building thermal dynamics. The proposed method uses systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our method incorporates structural prior knowledge from traditional physics-based building modeling into the architecture of the deep neural network model. Further, we also use penalty methods to provide inequality constraints, thereby bounding predictions within physically realistic and safe operating ranges. We observe that stable eigenvalues accurately characterize the dissipativeness of the system, and use a constrained matrix parameterization based on the Perron-Frobenius theorem to bound the dominant eigenvalues of the building thermal model parameter matrices. We demonstrate the effectiveness and physical interpretability of the proposed data-driven modeling approach on a real-world dataset obtained from an office building with 20 thermal zones. The proposed data-driven method can learn interpretable dynamical models that achieve high accuracy and generalization over long-term prediction horizons. We show that using only 10 days’ measurements for training, our method is capable of generalizing over 20 consecutive days. We demonstrate that the proposed modeling methodology is achieving state-of-the-art performance by significantly improving the accuracy and generalization compared to classical system identification methods and prior advanced methods reported in the literature. compared to prior state-of-the-art methods reported in the literature.

A data driven method for optimal sensor placement in multi-zone buildings

In this paper, we propose a data-driven methodology to identify the optimal placement of sensors in a multi-zone building. The proposed methodology is based on statistical tests that study the (in) dependence of measurements from various available sensors. The tests advice on a set of most dissimilar sensors to be retained, as they would convey the maximum information. The method starts with an initial setup that can provide measurements of every building zone to carry out this study; any of these sensors can be removed eventually to decrease costs in normal operation. The method has the advantages of being purely data driven and computationally efficient, as against several methods proposed in the scientific literature, that operate under the premise that detailed building models are available, to evaluate the number/position of the required sensors. This property makes the method scale to different buildings, in an expert free manner. The methodology can help towards better characterization of a building for optimal control and monitoring applications. It is validated against a widely used method – Kalman filtering with Grey-box models, using two different case studies. In both cases, the proposed approach agrees with the results using grey box models, suggesting that the method is reliable, while being quick and efficient.

Context-aware Model Predictive Control framework for multi-zone buildings

Today, Model predictive control (MPC) has largely been used to optimize energy consumption and maintain thermal comfort in buildings. However, to build an online MPC model in building, the dynamics of the physical system must be accurately modeled, which is a time-consuming and costly task. Neural network models help to overcome the modeling problems especially with the availability of historical data. This research presents a novel online data-driven control framework named Model Predictive Control via Genetic algorithm (MPC-GA) allowing the optimal operation of the heating, ventilation, and air conditioning system and has been experimentally validated in a multi-zone retail building. The MPC-GA combines an attention-based neural network time series multivariate prediction model with a MPC framework. The prediction model used a dual-stream neural networks based on multivariate time series of controlled and uncontrolled inputs. The attention mechanism is applied on controlled parameters to give them more weight to better predict the zone temperature. The prediction model is used as input for the optimization framework which minimizes: energy consumption, peak demand and discomfort during occupied hours under self-tuned setpoint, temperature ramp and equipment cycling constraints. A heuristic search algorithm using a genetic algorithm is used to solve the online data-driven MPC-GA models and obtain the future optimal combination settings of all controls for all the zones over a prediction horizon. The benchmark results showed that the MPC-GA outperforms RBC control systems with more than 50% and 80% reduction in energy consumption and discomfort respectively.

An experiment-based impulse response method to characterize airborne pollutant sources in a scaled multi-zone building

Promptly locating the airborne pollutant sources in indoor environments is of great importance for improving air quality and ensuring indoor safety, especially in the context of controlling airborne pathogens. However, most existing studies focused on short-range airborne transmission in close contact and identified pollution sources based on numerical simulations. In this study, an experiment-based impulse response method was established to estimate the source term in a multi-zone building, and uncertainty analysis (UA) was conducted by combining the errors from the sensors and systematic responses. An acrylic scaled multi-zone building was built to investigate and validate the proposed method. The prior impulse response vectors between the potential sources and sensor network were obtained through impulse response experiments. Next, four sets of representative experiments were conducted with the scaled model to evaluate the source identification method. The results showed that the source release rate and source location could be identified using the impulse response method, and most of the relative errors of the source release rate were within 30%. For source location identification, the location probabilities of the constant and dynamic real sources were 44% and 100%, respectively. This study provides a novel perspective for source term estimation (STE) in multi-zone environments.

Data pre-processing and optimization techniques for stochastic and deterministic low-order grey-box models of residential buildings

Grey-box models are data-driven models where the structure is defined by the physics while the parameters are calibrated using data. Low-order grey-box models of the building envelope are typically used for two main applications. Firstly, they are used as a control model in Model Predictive Control (MPC) where the thermal mass of the building is activated as storage (for instance in demand response). Secondly, they are used to characterize the thermal properties of the building envelope using on-site measurements. The influence of the data pre-treatment on the performance of grey-box models is hardly discussed in the literature. However, in real applications, information about data pre-processing by sensors or data acquisition systems is expected to be limited. Therefore, the influence of the sampling time, low-pass filters and anti-causal shift (also called data labeling) are analyzed for grey-box models in deterministic and stochastic innovation form. The influence on the optimizer performance is also investigated. The datasets are generated from virtual experiments using multi-zone building performance simulations of a residential building (in lightweight wooden construction) heated using different types of excitation signals. Results show that the parameters of deterministic grey-box models are significantly influenced by the training data while the data pre-treatment has a limited impact on the model and optimizer performance. Depending on the training data, the value taken by some parameters is not physically plausible. On the contrary, stochastic models are significantly influenced by the data pre-treatment, especially the sampling time, and less by the training data. The parameters can become non-physical for large sampling times. However, the anti-causal shift proves to be efficient to keep the parameters almost constant with increasing sampling times. Even though the parameter values of the deterministic model are less physically plausible, the simulation performance of deterministic models is higher than using the equivalent stochastic models. These results suggest that deterministic models seem better suited for MPC while stochastic models are better suited for the characterization of thermal properties (if suitable data pre-treatment is applied).

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

Abstract We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from the observed system’s dynamics, and the neural control law is optimized offline by leveraging the differentiable closed-loop system model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law’s parameters directly by backpropagating economic MPC loss through the learned system model. The control performance of the proposed DPC method is demonstrated in simulation using learned model of multi-zone building thermal dynamics.

Thermal Modeling of the Occupied Multi-Zone Buildings Taking into Account the Uncertainties of Occupant Behavior

Thermal Modeling of the Occupied Multi-Zone Buildings Taking into Account the Uncertainties of Occupant Behavior

Study on the distributed model predictive control for multi-zone buildings in personalized heating

To meet demands of smart heating development, the novel dynamic regulation methods of heating systems need to be pursued. This paper presents a model predictive control (MPC) approach for room temperature regulation in multi-zone buildings. Among them, a) For heating systems with nonlinearity and large time delay, a hybrid optimization algorithm consisting of the improved particle swarm optimization (IPSO) algorithm and Newton-Raphson (NR) method in online optimization of MPC for a single zone building is proposed. Compared with the quadratic programming method, it can identify successfully the optimal control vector. b) A distributed MPC approach is presented for multi-zone buildings existing hydraulic coupling. Firstly, Nash-optimization acts on searching the global equilibrium point in the distributed structure, then the hybrid optimization algorithm works in the online optimization of these local MPCs. Compared with the decentralized MPC, the control performance is improved through co-operating and communicating between subsystems via network to share information related to their future behavior. Besides, the global performance index in the distributed MPC is redefined as the weighted sum of energy consumption and thermal comfort, which provides the possibility to eliminate quickly the most unfavorable thermodynamic loop in heating systems and reduce energy consumption.

Simulation and Measurement of Energetic Performance in Decentralized Regenerative Ventilation Systems

Decentralized regenerative mechanical ventilation systems have acquired relevance in recent years for the retrofit of residential buildings. While manufacturers report heat recovery efficiencies over 90%, research has shown that the efficiencies often vary between 60% and 80%. In order to better understand this mismatch, a test facility is designed and constructed for the experimental characterization and validation of regenerative heat exchanger simulation models. A ceramic honeycomb heat exchanger, typical for decentralized regenerative ventilation devices, is measured in this test facility. The experimental data are used to validate two modeling approaches: a one-dimensional model in Modelica and a computational fluid dynamics (CFD) model built in COMSOL Multiphysics®. The results show an overall acceptable thermal performance of both models, the 1D model having a much lower simulation time and, thus, being suitable for integration in building performance simulations. A test case is designed, where the importance of an appropriate thermal and hydraulic modeling of decentralized ventilation systems is investigated. Therefore, the device is integrated into a multizone building simulation case. The results show that including component-based heat recovery and fan modeling leads to 30% higher heat losses due to ventilation and 10% more fan energy consumption than when assuming constant air exchange rates with ideal heat recovery. These findings contribute to a better understanding of the behavior of a growing technology such as decentralized ventilation and confirm the need for further research on these systems.

Streamlined CFD simulation framework to generate wind-pressure coefficients on building facades for airflow network simulations

Building energy modeling software generally comes with capable airflow network solvers for natural ventilation evaluation in multi-zone building energy models. These approaches rely on arrays of pressure coefficients representing different wind directions derived from simple box-shaped buildings without contextual obstructions. For urban or obstructed sites, or more complex building shapes, however, further evaluation is needed to avoid geometric oversimplification. In this study, we present an automated and easy-to-use simulation workflow for OpenFOAM-based exterior airflow simulations to generate arrays of pressure coefficients for arbitrary building shapes and contextual situations. The workflow is compared to other methods commonly used to obtain pressure coefficients for natural ventilation analysis. Finally, we assess for which climate zones and building types modelers should rely on more accurate CFD-based pressure coefficients and where it may be justifiable to rely on easier and readily available analytical approaches to determine pressure coefficients. Results suggest that existing workflows lead to significant error in predicted comfort hours for climates in the Global South and modelers should consider CFD-based facade pressure coeficients.

Annual performance analysis of the PV/T system for the heat demand of a low-energy single-family building

The interest in the energy efficiency of buildings and the integration of new technologies to reduce the heating and cooling loads through the building envelope is not new. The European Union (EU) is committed to decarbonising its building stock as almost 50% of the Union’s final energy consumption is for heating and cooling, of which 80% is used in buildings. The article analysis the performance of a PV/T system (with a heat mat) for the heat demand of a low-energy two-zone, single-family building during the year. For this purpose, a building model was built in TRNSYS using Multizone building model and TRNbuild in two variants of heating (internal - as reference and external). Then the building model was connected to a mechanical ventilation system providing heating during winter for which the heat source is a PV/T system to demonstrate if the heating requirements for the household can be provided during different seasons of the year. Additionally, it was indicated how much energy consumption reduction can be obtained in comparison to the case when a heating unit is used.

Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance

An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem of simultaneously identifying an aggregate thermal dynamic model and unknown disturbances from input–output data of multi-zone buildings. The unknown disturbance is a key challenge since it is not measurable but non-negligible. We first present a principled method to aggregate a multi-zone building model into a single zone model, and show the aggregation is not as trivial as it has been assumed in the prior art. We then provide a method to identify the parameters of the model and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data collected from a multi-zone building testbed in Oak Ridge National Laboratory. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain, even in the case of a specially designed test in which the disturbances affecting each zone are known (as the case of our experimental testbed). This insight is used to provide a heuristic that can be used to assess when the identification results are likely to have high or low accuracy.

Optimal Energy Dispatch Controller for Fuel Cell-Integrated Multi-Zone Building

Abstract Stationary fuel cells provide potential opportunities for energy savings when integrated with buildings. Through smart dispatch of both electrical power and heat generated by the fuel cells and managing the building loads, the buildings can achieve more efficient operation. In this paper, we develop an optimal energy dispatch controller to operate a fuel cell-integrated building. The controller leverages the inherent thermal storage and the dispatchable fuel cell to reduce its operating cost and to allow the building to participate in grid services. The proposed controller is implemented on two types of commercial buildings, a large office building and a large hotel, and the effectiveness of the controller is demonstrated through simulations. The results also indicate that the potential saving varies significantly with different system parameters, including season, fuel prices, and equipment sizing, which provide helpful insights for building operators and other stake holders.

Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO2 with OpenModelica Modeling

As most residents spend more than 90% of their time in buildings, acceptable and reasonable control of both indoor thermal comfort and air quality is imperative to ensure occupants’ health status and work productivity. However, current control strategies generally take either thermal comfort or indoor air quality as a single loop, rather than the concurrent control of two. To analyze their mutual influence, this study investigated the performance of three multi-control approaches, i.e., proportional integral derivative (PID) control of thermal comfort and a fixed outdoor air ratio, PID control of thermal comfort and design outdoor air rate, and PID control of thermal comfort and occupancy-based demand-controlled ventilation. As a pilot study, three typical control methods were implemented to a multi-zone building via OpenModelica modeling. The results indicate that indoor air temperature can be well-maintained under three control methods, however, the CO2 concentration under the fixed outdoor air ratio was over 1000 ppm, leading to poor indoor air quality. The control strategy with the design outdoor air rate could not properly ensure the CO2 concentration, due to the over-ventilated or under-ventilated phenomena, subsequently resulting in unnecessary energy waste. The occupancy-based demand controlled ventilation could maintain the CO2 concentration under the set-point with an intermediate power energy utilization.

IoT-Enabled Automatic Synthesis of Distributed Feedback Control Schemes in Smart Buildings

There is currently a rapid evolution of technologies enabling the “Internet-of-Things” paradigm, which leads to the design of systems with augmented sensing, analysis, and actuation capabilities. The intelligent control research community is beginning to recognize the challenge of designing flexible and adaptable control systems that will take advantage of the online evolution of the number and types of “things” in a large-scale system. Responding to this challenge, in a previous article, we designed and presented a semantically enhanced control system supervisor that integrates a knowledge graph and deductive inference capabilities, to achieve the online (re)configuration of feedback control schemes. In this article, we show that by exploiting the capabilities of this supervisor, we facilitate the automatic synthesis and plugging of model-based controllers for the space heating problem in multizone buildings. The supervisor retrieves stored knowledge about the building and the available control system components and uses it to configure online a scheme of distributed feedback controllers, one per building zone. The controllers are designed to utilize information online and optimally allocate the control signal to multiple heating devices in each building zone.

An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system

Numerous studies have demonstrated the benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, the construction and training of predictive models for building HVAC systems are widely recognized as a key technological barrier preventing large-scale adoption of EMPC for buildings. In this work, an encoder–decoder long short-term memory-based EMPC framework is developed. The key advantage of the approach is that a model may be automatically generated from a list of inputs and outputs. From the definition of inputs and outputs, the constructed model may be trained and automatically embedded into the EMPC framework for real-time estimation and control. The overall end-to-end EMPC framework from model training to on-line estimation and control are described. To this end, the encoder–decoder model provides a natural framework for state estimation (encoder), which is required to provide an initial condition for the predictive model of EMPC (decoder). Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated closed-loop system consists of a building zone from a multi-zone building, which is served by an air handling unit-variable air volume HVAC system. For the HVAC example considered, the trained encoder–decoder model can predict the indoor air temperature and HVAC sensible cooling rate of a building zone over a two-day horizon with high accuracy. Considering a time-of-use electric rate structure, the EMPC, which manipulates the zone temperature setpoint, can reduce the HVAC power consumption cost relative to keeping the zone temperature setpoint at its maximum value (i.e., minimum energy approach).