Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions

Research update, Spring 2018

Welcome.- Features.- Publications.- Events.- People.- Announcement

An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context

California is making major strides towards meeting its greenhouse gas emission reduction goals with the transformation of its electrical grid to accommodate renewable generation, aggressive promotion of building energy efficiency, and increased emphasis on moving toward electrification of end uses (e.g., residential heating, etc.). As a result of this activity, the State is faced with significant challenges of systemwide resource adequacy, power quality and grid reliability that could be addressed in part with demand responsive (DR) load modifying strategies using controllable building technologies. Dynamic facades have the ability to potentially shift and shed loads at critical times of the day in combination with daylighting and HVAC controls. This study explores the technical potential of dynamic facades to support net load shape objectives. A model predictive controller (MPC) was designed based on reduced order thermal (Modelica) and window (Radiance) models. Using an automated workflow (involving JModelica.org and MPCPy), these models were converted and differentiated to formulate a non-linear optimization problem. A gradient-based, non-linear programming problem solver (IPOPT) was used to derive an optimal control strategy, then a post-optimization step was used to convert the solution to a discrete state for facade actuation. Continuous state modulation of the façade was also modeled. The performance of the MPC controller with and without activation of thermal mass was evaluated in a south-facing perimeter office zone with a three-zone electrochromic window for a clear sunny week during summer and winter periods in Oakland and Burbank, California. MPC strategies reduced total energy cost by 9–28% and critical coincident peak demand was reduced by up to 0.58 W/ft2-floor or 19–43% in the 4.6 m (15 ft) deep south zone on sunny summer days in Oakland compared to state-of-the-art heuristic control. Similar savings were achieved for the hotter, Burbank climate in Southern California. This outcome supports the argument that MPC control of dynamic facades can provide significant electricity cost reductions and net load management capabilities of benefit to both the building owner and evolving electrical grid

Will the proposed regulatory reforms by the Basel com

У статті вивчаються перспективи реалізації Базельських рекомендацій (Базель ІІІ) на прикладі економік Єгипту та України. Зроблено висновок, що країни мають різні перспективи у цьому напряму. Значно кращі перспективи має Єгипет за причини більш стабільного розвитку банківської системи, діяльність якої до кризи у меньшому ступені залежала від зовнішніх запозичень.Стаття розглядалася у рамках другої онлайн конференції "Корпоративне управління та регулювання в банках", ДВНЗ "УАБС НБУ", 2-4 лютого 2011 р.The aim of this research is to assess the efficacy of the prospective reforms proposed by the Basel Committee on emerging market economies. Egypt and Ukraine are selected as comparative case studies representing middle-income developing nations and transition economies that have shown diverse reactions to the global crisis. Using a small-scale DSGE model, the projected changes to capital adequacy measures, minimum liquidity requirements and Corporate Governance are tested on a set of macroeconomic outputs: GDP growth, employment, inflation and interest rates over the period of 2000:01-2010:03. The results reveal that the DSGE model is an inaccurate forecasting tool for both nations. Also, the impacts of the proposed regulatory reforms are quite detrimental for Ukraine, but better weathered by the Egyptian economy, implying that emerging nations that were well geared up through meeting requirements of Basel II will show more resilience to the costliness of future reforms.Видавнича компанія "Віртус Інтерпрес", м. Сум

The New Basel Capital Accord and Questions for Research

The New Basel Accord for bank capital regulation is designed to better align regulatory capital to the underlying risks by encouraging better and more systematic risk management practices, especially in the area of credit risk. We provide an overview of the objectives, analytical foundations and main features of the Accord and then open the door to some research questions provoked by the Accord. We see these questions falling into three groups: what is the impact of the proposal on the global banking system through possible changes in bank behavior; a set of issues around risk analytics such as model validation, correlations and portfolio aggregation, operational risk metrics and relevant summary statistics of a bank’s risk profile; issues brought about by Pillar 2 (supervisory review) and Pillar 3 (public disclosure).Bank capital regulation, risk management, credit risk, operational risk

Short and long-term wind turbine power output prediction

In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature, and the wind direction. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model

Development of robust building energy demand-side control strategy under uncertainty

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.Ph.D.Committee Chair: Augenbroe, Gofried; Committee Member: Brown, Jason; Committee Member: Jeter, Sheldon; Committee Member: Paredis,Christiaan; Committee Member: Sastry, Chellur

Multiple-Model Adaptive Control With Set-Valued Observers

This paper proposes a multiple-model adaptive control methodology, using set-valued observers (MMAC-SVO) for the identification subsystem, that is able to provide robust stability and performance guarantees for the closed-loop, when the plant, which can be open-loop stable or unstable, has significant parametric uncertainty. We illustrate, with an example, how set-valued observers (SVOs) can be used to select regions of uncertainty for the parameters of the plant. We also discuss some of the most problematic computational shortcomings and numerical issues that arise from the use of this kind of robust estimation methods. The behavior of the proposed control algorithm is demonstrated in simulation.Comment: Combined 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, 200

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted