State-Space Modeling of Thermal Spaces in a Multi-Zone Building

A study on system identification and modeling of thermal spaces in a large institutional building is presented. The main topic of this paper is how the optimum model order associated which each thermal zone depends on factors such as the location of the zone within the building, its orientation and its exposure to outdoor space. Thermal models are essential in predictive control since they are required to predict the thermal load of a single building zone, a collection of various thermal spaces, or a whole building. The results of this study will serve as a guideline for choosing the appropriate order of linear models in similar buildings. The case study building is a model of a two storey school with a floor area of 24,000 m2 (258,000 ft2). The detailed thermal model of the building is created in EnergyPlus. This building model consists of 46 thermal zones covering a large variety of spaces: small offices, classrooms, long hallways and two gymnasia. The EnergyPlus is used to generate yearly input and output data available at 10-minute intervals; this data is used in a methodical system identification exercise, resulting in a set of multi-input single-output (MISO) state-space linear models. The challenge in modeling the thermal zones is to develop a relatively low-order model such that the thermal response of each zone is calculated by incorporating the effect of diverse inputs, such as outdoor factors (solar gains and outdoor temperature) as well as indoor factors, e.g., internal gains and heating and cooling energy delivered to the zone. Moreover, in a multi-zone building, accounting for the thermal effect of adjacent zones on one another is also an important factor to be taken into account. It has been found that this additional complexity requires careful selection of the inputs to the linear models, e.g., it might be helpful to include the heating/cooling delivered to adjacent zones

A Multi-level MPC Simulation Study in a School Building

This paper presents results obtained by applying a multi-level methodology for the implementation of a model-predictive control (MPC) strategy in a large institutional building. The case study building, is a model of a two storey school building, with a floor area of 24,000 m2 (258,000 ft2) with 46 thermal zones. The zones considered include a large diversity of spaces: small offices, classrooms, long hallways and two gymnasia. A detailed thermal model of the building was created in EnergyPlus. The EnergyPlus was used to generate input and output data employed for a systematic system identification exercise, which resulted in a set of multi-input single-output (MISO) linear models. Three control levels were considered: a thermal zone level (46 models), “wing†level (7 models) and a building level (one model). The models identified are state-space representations with order ranging between 4 and 12. This hierarchical, multi-level methodology enables the use of low-order models for each system under consideration: for example, a simple 9th order model at the building level can be used to predict its thermal load over a 48-h horizon, with a relatively coarse sampling time of 2 hours (24 samples). At the other extreme, a zone level model has a prediction horizon of 2 hours, and a much finer sampling time of 10 minutes (12 samples). For the MPC studies, a mechanical system considering thermal energy storage devices (ice bank + hot water tank) was considered in the calculations. An optimization routine was carried out to minimize the electricity cost, while maintaining comfortable conditions in the space: a time-of-use rate was employed in the definition of the objective function. The results presented in this paper illustrate how the multi-level concept discussed in this paper can be used to harmonize the performance of building control systems, from the supervisory BEMS to the local thermostat controllers

Order reduction of IIR and FIR filters using control methods with applications in DSL networks

In this thesis, the problem of modeling a Digital Subscriber Line (DSL) network is investigated. Any changes made to the DSL network such as addition or relocation of customers may affect the performance of the overall network. Therefore, it is very useful to have a laboratory model to test the effect of every change made to the network easily. The model can also be used by service providers to test the quality of service before assigning a line to a new customer, reducing the trouble-shooting cost. The modeling consists of extracting the magnitude response of a particular DSL channel, fitting the data to a discrete-time finite dimensional LTI model and reducing its order reasonably. The resultant filter will be a suitable model for laboratory implementations. The magnitude response of the DSL network can be computed numerically using general and small-scale models available for transmission lines. The computation can be generalized to the distributed networks. Digital filter approximation can be accomplished using the conventional methods. A gradient-based method is also proposed in order to generate a FIR filter. The model order reduction is performed using Balanced Realization technique which is applicable to all stable finite dimensional LTI models. It is also accomplished by minimizing either mean-squared error or infinity norm of error. The performance of each method is evaluated using standard test loops. A reasonable range of model order for each test loop is also discussed based on the configuration of each loop, which can be used as a general guide for any wired transmission network

Investigation of Soil Contamination With Cryptosporidium spp. Oocysts in DifferentRegions of Yazd, Central Iran

Background: Cryptosporidium species are coccidian parasites that cause gastrointestinal disorders in humans and other animals worldwide.Objective: The aim of this study was to demonstrate the rate of contamination with Cryptosporidium spp. oocysts in soils collected from public parks, primary schools, green areas, kindergartens, suburban areas, streets, residential complexes, backyards and a passenger terminal in Yazd, central Iran.Materials and Methods: This cross-sectional study was conducted from September 2014 to February 2015, and the samples were collected from 9 different study sites and 56 regions. Soil samples were investigated by flotation technique and modified Ziehl–Neelsen staining for Cryptosporidium spp. oocysts. Finally, the slides were examined with a light microscope. The data were analyzed using SPSS software version 20.0 and chi-square statistical test.Results: Of a total of 220 soil samples, 47 (21.36%) were found to contain Cryptosporidium spp. oocysts. Statistical analysis showed that there was no significant difference between the contamination rate and different study sites in Yazd, central Iran (P > 0.05). The highest contamination rate was observed in public parks (38.3%) and the lowest in passenger terminal, kindergartens and streets (4.25%) (P = 0.934).Conclusion: The results of the present study show that the contamination of soil with Cryptosporidium spp. can be considered a serious problem in Yazd, central Iran. It should be considered particularly in public parks

Preliminary Assessment of a Weather Forecast Tool for Building Operation

Although the potential of model predictive control (MPC) for the operation of buildings is widely recognized, as of today its adoption has been rather limited. This is partly due to the lack of user-friendly software tools for MPC, such as tools to facilitate the incorporation of forecast information in building automation systems. In view of this, CanmetENERGY, a research centre of Natural Resources Canada, has developed CanMETEO, a software tool free of charge aimed at obtaining weather forecast data and make it available in a useful and practical format for building operators. CanMETEO, which was released officially in August 2017, uses raw data produced by the Meterological Service of Environment Canada. This data, with high spatial resolution (e.g., 2 km x 2 km grids, and even denser for urban areas) enables the possibility of obtaining forecasts for very specific locations in the Canadian territory. Hundreds of weather variables (such as temperature, humidity, wind speed, cloud cover, among many others) are available for each point, which can be selected by the user via a graphical interface. The data is converted from GRIB files (a standard binary format used by meteorologists) into comma-separated value (CSV) files, which can be easily accessed. New forecasts become available every 6 hours, with a prediction horizon of 48 hours at hourly time steps; the retrieval of new weather forecasts can be setup in order to be performed automatically. These continuously updated CSV files may then be easily incorporated into building operation algorithms or simple optimization routines. Once the basic variables are obtained, post-processing calculations are applied in order to estimate solar irradiance on any given plane required by the user, for example, building façades and building-integrated photovoltaic panels. This feature also makes it possible to estimate the effect of solar gains on the thermal response of a building, and to estimate the output of photovoltaic panels. A preliminary evaluation of the tool, based on on-site measurements, is presented in this paper. It is expected that CanMETEO (currently used by Canadian research centre and universities) will provide one further step to the widespread adoption of predictive control as a viable, popular solution in building operation

Fungal contamination of indoor public swimming pools and their dominant physical and chemical properties

Introduction: Considering to the existence of both parasitic and fungal pathogens in the indoor public swimming pools and non-utilization of suitable filtration and disinfection systems in these places, this research aimed to determine the relationship between the indoor public swimming pools and possible pollution with parasitic and fungal agents, as well as physical and chemical characteristics of these pools and compare the results with national standards. Methods: In this study, 11 active indoor swimming pools of Zahedan city were sampled, using plastic pumps techniques, at the middle of winter to the late summer season. A total of 88 water samples (eight water samples from each pool) were examined to determine the residual chlorine, contamination with parasitic and fungal agents, using culture media and slide culture techniques. Results were analyzed with SPSS software (V16) and, Microsoft Excel (V2010). Results: The findings revealed parasitic fungal contamination with Cladosporium, Penicillium, Aspergillus flavus and Aspergillus fumigatus, etc. and the physicochemical factors comply with the minimum standards had which indicates the need for continuous monitoring and control of water filtration and disinfection of water is swimming. Conclusion: The results show reasonable derangement of physicochemical and microbial factors of the evaluated pools. Efforts shall be made by the concerned authorities to provide health education to users, quality water at the pools and to maintain the safety and quality of the water through proper and adequate chlorination

Managing uncertainty in robust controller implementation

For a linear time-invariant (LTI) plant, a robust controller is often designed in order to maintain robust stability and performance of the system. In robust controller implementation, two problems might arise. First, it is interesting to know whether it is possible to simplify the controller and to what degree before its implementation while still maintaining system robust performance. The second concern arises in real-time applications where the controller may have to be retuned or redesigned several times after the initial implementation stage. In both problems, it is important to know how much a modified controller can deviate from the originally designed one in the frequency domain. The robust controller deviation is modeled as the controller uncertainty just like any other uncertainty with its own unique weighting function. A necessary and sufficient condition for robust performance as well as a sufficient condition is derived in the form of bounds on the magnitude of maximum deviation of the robust controller over a grid of frequency samples for a single-input single-output (SISO) structure. The multi-input multi-output (MIMO) case is also addressed by providing an upper bound on the maximum singular value of the system's frequency response derived at individual frequency samples, leading to a sufficient condition for robust performance. The above bounds are used in conjunction with the balanced truncation technique in order to determine how much the order of a robust controller can be reduced without losing robust performance. The order reduction limit is expressed as the maximum number of states that can be removed in a state-space realization of the robust controller for both SISO and MIMO system structures, without actually having to reduce the order. Finally, a practical approach is proposed in order to derive a robust internal model (IMC) controller for an SISO system structure. The ideal robust IMC controller is already provided as a function of the frequency response of all system components at every frequency sample. It is then approximated with a stable and preferably low order system while maintaining robust performance in three steps - the inverse fast Fourier transform (IFFT), the finite impulse response (FIR) approximation, and the FIR-to-IIR (infinite impulse response) conversion.Un contrôleur robuste est souvent conçu afin de maintenir la stabilité et la performance robuste d'un système linéaire invariant dans le temps. Durant l'implantation d'un tel contrôleur, deux problèmes se présentent. Premièrement, il est intéressant de savoir s'il est possible de simplifier le contrôleur robuste et si oui, jusqu'à quel point avant de l'implanter, tout en garantissant la performance robuste. Le deuxième problème se pose dans les applications à temps réel où le contrôleur devrait être refait ou ajusté plusieurs fois après la mise en service initiale. Il est important de savoir, concernant les deux problèmes ci-dessus, jusqu'à quel point le contrôleur modifié peut s'éloigner de l'original dans le domaine des fréquences. Une condition suffisante et nécessaire pour la performance robuste ainsi qu'une condition suffisante sont dérivées sous forme de limites sur le module de la déviation maximum du contrôleur robuste sur une grille de fréquences pour un système à sortie unique et à entrée unique (SISO). Le cas d'entrées et sorties multiples (MIMO) est aussi traité en fournissant une limite sur la valeur singulière maximum de la réponse en fréquence du système calculée à chaque point de fréquence, formant une condition suffisante pour la performance robuste. Les limites ci-dessus sont utilisées avec la technique de réduction équilibrée (balanced truncation) afin de déterminer à quel point il est possible de réduire l'ordre du contrôleur sans perdre la performance robuste. Le nombre maximum d'états du contrôleur admissibles pour l'élimination est donné, sans vraiment devoir le modifier. Finalement, une méthode pratique est proposée pour réaliser un contrôleur robuste basé sur la commande par modèle interne (IMC) pour un système SISO. La réponse en fréquence du contrôleur robuste idéal est déjà fournie comme une fonction de la réponse en fréquence de toutes les composantes de système. Puis, le contrôleur idéal est approximé par un système stable et préférablement d'ordre peu élevé en gardant la performance robuste aux trois étapes : transformation inverse de Fourier rapide (IFFT), approximation par un système de réponse impulsionnelle finie (FIR) et conversion de FIR à réponse impulsionnelle infinie (IIR)

Frequency-Domain Robust Performance Condition for Controller Uncertainty in SISO LTI Systems: A Geometric Approach

This paper deals with the robust performance problem of a linear time-invariant control system in the presence of robust controller uncertainty. Assuming that plant uncertainty is modeled as an additive perturbation, a geometrical approach is followed in order to find a necessary and sufficient condition for robust performance in the form of a bound on the magnitude of controller uncertainty. This frequency domain bound is derived by converting the problem into an optimization problem, whose solution is shown to be more time-efficient than a conventional structured singular value calculation. The bound on controller uncertainty can be used in controller order reduction and implementation problems