Model predictive control (MPC), although considered a high-potential control approach, usually requires considerable effort for model-creation and parametrization. Moreover, many models can be too ...computationally intensive for control applications. Distributed model predictive control (DMPC) is a promising approach that avoids the construction of a complex model of the total system and thus facilitates modeling and supports the use of exact simulation models.
DMPC divides the optimization problem into sub-problems, with the advantage that pre-fabricated simulation models from standard libraries, models provided by component manufacturers or purely data-driven models can be used. Moreover, each optimization problem, considered for its own, becomes smaller and, in whole, can be solved faster compared to an integrated system model.
The distributed optimizations must be coordinated to achieve near-global-optimum performance. This coordination requires a suitable scheme, which, in many publications, is based on iterative data exchange between the subsystems. In previous works, we developed a non-iterative algorithm based on the exchange of lookup tables. In this paper, we compare and benchmark the previously developed approach against a second iterative approach to show advantages and limitations of both algorithms.
We apply both approaches to the Modelica simulation model air-handling unit while using artificial neural network models and a non-linear solver for the DMPC algorithms. We make simplifying assumptions to provide mathematical justification regarding the optimality of the approaches. Judging from the indicators for control quality and the monetary operation costs in the case study, we conclude that the algorithms hold high potential for the application in generic building energy systems.
•Distributed model predictive control (DMPC) algorithms facilitate the construction or training of models.•Study of a suitable schemes for data exchange between subsystems.•Simulation study with an air handling unit modelled in Modelica.•Usage of artificial neural network models for model-predictive control.•Conclusion on the high potential of DMPC in generic building energy systems.
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•Model-Free, Agent-based Control and Optimization Solution.•Energy efficient building management preserving acceptable comfort Levels.•Real-life application results during heating ...period.•Significant improvements respect to a common commercial control solution.
A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized1The terms “decentralized”, “distributed” and “agent-based” are considered to have similar meanings herein.1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based, model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.
In plant pararetroviruses, pregenomic RNA serves both as a template for replication through reverse transcription and a polysictronic mRNA. This RNA has a complex leader sequence preceding the first ...large ORF. The leader contains multiple short ORFs and strong secondary structure, both inhibiting ribosome scanning. Translation on this RNA is initiated by shunting, in which scanning ribosomes bypass a large portion of the leader with the inhibitory secondary structure and short ORFs. In Cauliflower mosaic virus (CaMV), the ribosome shunting mechanism involves translation of the 5'-proximal short ORF terminating in front of the secondary structure that appears to force ribosomes to take off and resume scanning at a landing site downstream of the structure. Using two plant protoplast systems and shunt-competent wheat-germ extracts, we demonstrate that in Rice tungro bacilliform virus (RTBV) shunting also depends on the first short ORF followed by strong secondary structure. Swapping of the conserved shunt elements between CaMV and RTBV revealed the importance of nucleotide composition of the landing sequence for efficient shunting. The results suggest that the mechanism of ribosome shunting is evolutionary conserved in plant pararetroviruses.
Acquiring knowledge from the growing amount of Building Automation and Control Systems (BACS) data is becoming a more and more challenging and complex engineering task. However, it is also a ...prerequisite for smart and sustainable energy management as well as improving energy efficiency and comfort of building users. This report analyses the prospects of applying selected supervised learning methods for time series classification in BACS. Our training and testing data covered multivariate time series from 5,142 data points located in E.ON Energy Research Center building, describing observations from 22 classes, such as temperatures of gaseous fluid, CO2 concentrations, heat flows, and operating messages. We trained thirteen types of classifiers: complex tree, medium tree, simple tree, linear Support Vector Machines, quadratic Support Vector Machines, boosted trees, bagged trees, subspace discriminant, subspace KNN, RUSBoosted Trees, Fine KNN, Coarse KNN and random forests. The highest demonstrated average classification accuracy concerned bagged trees (56.76%), with the maximum accuracy level of 76.54%. However, the maximum accuracy achieved by random forests was even higher, reaching 78.95%. Finally, we identified factors that may have a substantial influence on performance of particular methods.
The pregenomic 35 S RNA of cauliflower mosaic virus (CaMV) belongs to the growing number of mRNAs known to have a complex leader sequence. The 612-nucleotide leader contains several short open ...reading frames (sORFs) and forms an extended hairpin structure. Downstream translation of 35 S RNA is nevertheless possible due to the ribosome shunt mechanism, by which ribosomes are directly transferred from a take-off site near the capped 5′ end of the leader to a landing site near its 3′ end. There they resume scanning and reach the first long open reading frame. We investigated in detail how the multiple sORFs influence ribosome migration either via shunting or linear scanning along the CaMV leader. The sORFs together constituted a major barrier for the linear ribosome migration, whereas the most 5′-proximal sORF, sORF A, in combination with sORFs B and C, played a positive role in translation downstream of the leader by diverting scanning ribosomes to the shunt route. A simplified, shunt-competent leader was constructed with the most part of the hairpin including all the sORFs except sORF A replaced by a scanning-inhibiting structure. In this leader as well as in the wild type leader, proper translation and termination of sORF A was required for efficient shunt and also for the level of shunt enhancement by a CaMV-encoded translation transactivator. sORF A could be replaced by heterologous sORFs, but a one-codon (start/stop) sORF was not functional. The results implicate that in CaMV, shunt-mediated translation requires reinitiation. The efficiency of the shunt process is influenced by translational properties of the sORF.
Optimizing the operation of energy systems as typically found in buildings, plants, and districts has the potential to greatly reduce primary energy consumption and maintenance expenses. Due to the ...complexity and nontransparency of these systems, costs of implementing optimizations may well consume or exceed potential savings. Data-driven methods for the automatic analysis and optimization of energy systems represent a promising solution to this dilemma. In this paper, we present a real-world IoT cloud platform for automatic data-driven analysis and control of energy systems that promotes usability, scalability, and efficiency. The presented solution has been successfully developed to market maturity in agile collaboration with industry and research, and is now commercially offered by aedifion GmbH.
Universally applicable building automation and control systems could contribute largely to reducing both the environmental impact and the operation costs of buildings. We present a building HVAC ...control algorithm that excites small subsystems and uses the system responses to calibrate prefabricated Modelica models. It uses these models for distributed model-assisted online control. We demonstrate and evaluate the algorithm in simulations and real-life experiments on a central air handling unit. Results show that the algorithm is capable of controlling the systems. Its performance is comparable to the reference scenario based on PI controllers.
Cauliflower mosaic virus (CaMV) is a DNA-containing pararetrovirus replicating by means of reverse transcription of a terminally redundant pregenomic 35S RNA that is also used as a polycistronic ...mRNA. The leader of 35S RNA is long, highly structured, and contains multiple short ORFs (sORFs), which strongly interfere with the ribosome scanning process. Translation of this RNA is initiated by a ribosome shunt mechanism, in which ribosomes translate the most 5′-proximal short ORF (sORF A), then skip a large region of the leader containing a putative RNA encapsidation signal and reinitiate translation at the first long viral ORF. Here, we demonstrate that the efficiency of the sORF A-mediated ribosome shunt is an important determinant of viral infectivity. Point mutations in sORF A, which reduced the basal level of shunt-dependent expression and the degree of shunt enhancement by a CaMV-encoded translation transactivator (TAV), consequently reduced infectivity of the virus in turnip plants. First- or second-site reversions appeared in the viral progeny. The second-site reversions restored shunt-dependent expression to an extent correlating with their relative abundance in the progeny. Mutations that abolished both the basal and TAV-activated components of shunting proved to be lethal. Finally, by using an artificial stem structure that blocks scanning, we obtained direct evidence that ribosome shunt operates during CaMV infection.
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