Passive Design of Buildings for Extreme Weather Environment

Buildings account for nearly 40% of the end-use energy consumption and carbon emissions globally. Buildings, once built, are used at least for several decades. The building sector therefore holds a significant responsibility for implementing strategies to increase energy efficiency and reduce carbon emissions and thus contribute to global efforts directed toward mitigating the adverse effects of climate change. The work presented in this paper is a part of continuing efforts to identify, analyze and promote the design of low energy, sustainable buildings with special reference to the Kazakhstan locality. Demonstration of improved environmental conditions and impact on energy savings will be outlined through a case study incorporating a passive design approach and detailed computational fluid dynamics analysis for an existing building complex. The influence of orientation and configuration is discussed with reference to energy efficiency and associated wind comfort and safety. The effect of these aspects on energy consumption and comfortable wind environment has been assessed using CFD analysis and proved to be affective. Single building and multiple building configurations have been analyzed and compared. According to the findings, multiple building configurations have better wind conditions when compared with a single standing building. With respect to orientation the former one should be modeled with the fully surrounded side of a “box” opposite to the predominant wind direction whereas the latter one should be located with the rear side opposite to the wind direction. Thus, results indicated that there is a considerable influence of passive design and orientation on energy efficiency, wind comfort and safety. Careful consideration and application of the findings can potentially lead to considerable decrease of energy consumption and, therefore, allow saving money and the environment at the same time

A Multi-Agent Control Approach for Optimization of Central Cooling Plants

This paper presents an application of a multi-agent control approach for supervisory control of large central cooling plants. The starting point for this work was a multi-agent control simulation framework developed by Cai (2015).  To adapt the framework to the problem at hand several tasks were accomplished: agents representing the performance of the different devices of the plant were developed and inserted in the framework and generalized heuristics were incorporated to make the approach less computationally intensive. A case study of an existing cooling plant with significant complexity was utilized to conduct an extensive evaluation of the approach in terms of optimality and computational resources. Simulations were carried out using one year of historical data to predict the performance of the plant under three different control strategies: 1) multi-agent control, 2) centralized optimization based on mathematical programming techniques and 3) a heuristic control strategy. The results showed that significant savings can be achieved through the implementation of multi-agent control. It is expected that, if each hardware component of the plant comes with an integrated agent that represents its behavior, then the proposed multi-agent framework could automatically generate the multi-agent structure and control algorithm after some relatively simple pre-configuration steps. This will reduce the site-specific engineering and will provide a more economic and easy to configure solution for central cooling systems

Passive Design of Buildings for Extreme Weather Environment

Buildings account for nearly 40% of the end-use energy consumption and carbonemissions globally. Buildings, once built, are used at least for several decades. The building sectortherefore holds a significant responsibility for implementing strategies to increase energyefficiency and reduce carbon emissions and thus contribute to global efforts directed towardmitigating the adverse effects of climate change. The work presented in this paper is a part ofcontinuing efforts to identify, analyze and promote the design of low energy, sustainable buildingswith special reference to the Kazakhstan locality. Demonstration of improved environmentalconditions and impact on energy savings will be outlined through a case study incorporating apassive design approach and detailed computational fluid dynamics analysis for an existingbuilding complex. The influence of orientation and configuration is discussed with reference toenergy efficiency and associated wind comfort and safety. The effect of these aspects on energyconsumption and comfortable wind environment has been assessed using CFD analysis and provedto be affective. Single building and multiple building configurations have been analyzed andcompared. According to the findings, multiple building configurations have better wind conditionswhen compared with a single standing building. With respect to orientation the former one shouldbe modeled with the fully surrounded side of a “box” opposite to the predominant wind directionwhereas the latter one should be located with the rear side opposite to the wind direction. Thus,results indicated that there is a considerable influence of passive design and orientation on energyefficiency, wind comfort and safety. Careful consideration and application of the findings canpotentially lead to considerable decrease of energy consumption and, therefore, allow savingmoney and the environment at the same time

Optimal tuning of proportional integral derivative controller for simplified heating ventilation and air conditioning system

A Heating Ventilation and Air Conditioning system (HVAC) is an equipment that is designed to adapt and adjust the humidity as well as temperature in various places. To control the temperature and humidity of the HVAC system, various tuning methods such as Ziegler–Nichols (Z-N), Chien-Hrones-Reswick (CHR), trial and error, robust response time, particle swarm optimization (PSO) and radial basis function neural network (RBF-NN) were used. PID is the most commonly used controller due to its competitive pricing and ease of tuning and operation. However, to effectively control the HVAC system using the PID controller, the PID control parameters must be optimized. In this work, the epsilon constraint via radial basis function neural network method is proposed to optimize the PID controller parameters. The advantages of using this method include fast and accurate response and follow the target values compared to other tuning methods. This work also involves the estimation of the dynamic model of the HVAC system. The non-linear decoupling method is used to modify the model of HVAC system. The benefits of using the proposed simplification technique rather than other techniques such as the relative gain array techniques (RGA) is because of its simplification, accuracy, and reduced non-linear components and interconnection effect of the HVAC system. It is observed that the amount of integral absolute error (IAE) for temperature and humidity based on the simplified model are decreased by 18% and 20% respectively. Moreover, it is revealed that optimization of PID controller through multi objective epsilon constraint method via RBF NN of the simplified HVAC system based on non-linear decoupling method shows better transient response and reaches better dynamic performance with high precision than other PID control tuning techniques. The proposed optimum PID controller and estimation of dynamical model of the HVAC system are compared with the different tuning techniques such as RBF and ZN based on original system. It is observed that the energy cost function due to temperature (JT) and humidity (JRH) are lowered by 15.7% and 4.8% respectively; whereas the energy cost functions reflect the energy consumptions of temperature and humidity which are produced by the humidifier and heating coil. Therefore, based on the new optimization method the energy efficiency of the system is increased. The unique combination of epsilon constraint method and RBF NN has shown that this optimization method is promising method for the tuning of PID controller for non-linear systems

Management model for energy efficiency - Intelligent System module

The power consumption in buildings represents a 30-40% of the final energy usage, hence it is necessary to minimize the power consumption by optimizing the operation of several loads without impacting in the customer’s comfort. According to the above in this work an intelligent approach framing in a management model is presented for the power consumption management of devices taking into account some variables as indoor temperature, outdoor temperature, illuminance and presence. Furthermore, in this research the integration of several Demand Side Management (DSM) criteria with one criterion based on neural networks and other inspired on differential tariff is carried out through dynamic and intelligent selections according to variables performance and customer´s preferences, e.g. priority list of criteria, operation based on comfort or consumption, in addition to other preferences as temperature. Likewise, a previous diagnosis analysis through energy audit is carried out to evaluate devices performance and customer habits. Experimental testing to the proposed approach has been performed in an environment object of study with the consumption data base and its performance tested in simulations runs. The testing results show that energy savings can be achieved through of recommendations provided by energy audit and proposed states by dynamic manager.MaestríaMagister en Ingeniería Electrónic

A Model for Performance Evaluation of Climate-Adaptive Building Envelopes Using Parametric Models and Multi-Criteria Optimization

The goal of this research is to enable designers to evaluate the performance of Climate-Adaptive Building Envelopes (CABE) to make better decisions at the conceptual design stage. This goal was accomplished by delivering three contributions to the fields of parametric modeling, building performance simulation, and multi-criteria optimization. There are three main challenges in CABE performance evaluation that cannot be overcome by conventional methods: 1) defining a suitable relationship between environmental factors and their thresholds by focusing on a given condition in CABE behavior control; 2) representing a CABE’s time-series behavior by using a single Building Performance Simulation (BPS) model; and 3) managing information related to a CABE’s performance and behavior for use in design decisions. To overcome these issues, this research developed a new CABE performance evaluation method called Parametric Behavior Maps (PBM), which makes three key contributions. First, the PBM method is able to generate a CABE operation schedule as an Hourly Behavior of Openness (HBOO) scenario to evaluate CABE performance using a single BPS model. Second, the PBM method produces more reliable outcomes than the conventional process, especially in terms of the time-lag effect of thermal performance. Third, the use of a Function-based Behavior Control System (FBCS) for the CABE efficiently facilitates a multi-criteria optimization process by progressively simulating alternative HBOO scenarios, allowing designers to choose the best scheme. These three contributions offer logical proof that the use of parametric modeling and simulation tools can help designers make better decisions regarding CABE alternatives. The PBM method was validated by investigating several test cases. First, static shading scenarios were developed using the PBM; the amount of incoming solar radiation was then compared with outcomes from the BPS with static shading. Second, indoor temperature profiles were simulated using the PBM method and an HBOO scenario; the results were compared with the outcomes obtained from the existing method, in order to determine the PBM’s reliability. Third, the integration of the PBM method and evolutionary multi-objective optimization technique illustrates the usefulness of the FBCS in CABE performance optimization

Final Causality in the Thought of Thomas Aquinas

Throughout his corpus, Thomas Aquinas develops an account of final causality that is both philosophically nuanced and interesting. The aim of my dissertation is to provide a systematic reconstruction of this account of final causality, one that clarifies its motivation and appeal. The body of my dissertation consists of four chapters. In Chapter 1, I examine the metaphysical underpinnings of Aquinas’s account of final causality by focusing on how Aquinas understands the causality of the final cause. I argue that Aquinas holds that an end is a cause because it is the determinate effect toward which an agent’s action is directed. I proceed by first presenting the general framework of causality within which Aquinas understands final causality. I then consider how Aquinas justifies the reality of each of the four kinds of cause, placing special emphasis on the final cause. In Chapter 2, I consider final causality from the perspective of goodness and explore the reasons why Aquinas thinks that the end of an action is always good. For even if one was convinced that the end of an action is indeed a cause, one might still resist attributing any normative or evaluative properties to the end, much less a positively-valenced normative property like goodness. In this chapter, I show how, given Aquinas’s metaphysics of powers and his characterization of goodness as that which all desire, it follows that every action is for the sake of some good. In Chapter 3, I consider Aquinas’s account of the relation between final causality and cognition. In many passages throughout his corpus—most famously in the fifth of his Five Ways—Aquinas advances the claim that cognition plays an essential role in final causality. In this chapter, I explore Aquinas’s account of the relation between final causality and cognition by reconstructing his Fifth Way and investigating the metaphysical foundations on which it rests. While the first three chapters of my dissertation focus on Aquinas’s account of final causality from the perspective of the ends of individual agents, in Chapter 4 I broaden my focus to consider the way in which the account of final causality developed in these earlier chapters shapes Aquinas’s philosophical cosmology. I argue that, on Aquinas’s view, when an individual agent acts for an end, it is plays a role in a larger system, e.g. a polis, an ecosystem, or the universe itself

A Model for Performance Evaluation of Climate-Adaptive Building Envelopes Using Parametric Models and Multi-Criteria Optimization

The goal of this research is to enable designers to evaluate the performance of Climate-Adaptive Building Envelopes (CABE) to make better decisions at the conceptual design stage. This goal was accomplished by delivering three contributions to the fields of parametric modeling, building performance simulation, and multi-criteria optimization. There are three main challenges in CABE performance evaluation that cannot be overcome by conventional methods: 1) defining a suitable relationship between environmental factors and their thresholds by focusing on a given condition in CABE behavior control; 2) representing a CABE’s time-series behavior by using a single Building Performance Simulation (BPS) model; and 3) managing information related to a CABE’s performance and behavior for use in design decisions. To overcome these issues, this research developed a new CABE performance evaluation method called Parametric Behavior Maps (PBM), which makes three key contributions. First, the PBM method is able to generate a CABE operation schedule as an Hourly Behavior of Openness (HBOO) scenario to evaluate CABE performance using a single BPS model. Second, the PBM method produces more reliable outcomes than the conventional process, especially in terms of the time-lag effect of thermal performance. Third, the use of a Function-based Behavior Control System (FBCS) for the CABE efficiently facilitates a multi-criteria optimization process by progressively simulating alternative HBOO scenarios, allowing designers to choose the best scheme. These three contributions offer logical proof that the use of parametric modeling and simulation tools can help designers make better decisions regarding CABE alternatives. The PBM method was validated by investigating several test cases. First, static shading scenarios were developed using the PBM; the amount of incoming solar radiation was then compared with outcomes from the BPS with static shading. Second, indoor temperature profiles were simulated using the PBM method and an HBOO scenario; the results were compared with the outcomes obtained from the existing method, in order to determine the PBM’s reliability. Third, the integration of the PBM method and evolutionary multi-objective optimization technique illustrates the usefulness of the FBCS in CABE performance optimization

A systematic approach of integrated building control for optimization of energy and cost

More efficient building energy management leads to lower energy consumption and cost, higher occupancy comfort and less detrimental effects on the environment. Improving building energy management with advanced integrated building control provides a tool to coordinate and optimize control of multiple indoor parameters by considering their interconnected effects on building energy consumption and comfort. A building integrated optimization requires an approach to calculate building energy consumption, operate in real time, optimize building control parameters, and be able to modify systems operations or schedules in response to environmental or demand response signals inputs. The integrated optimization has significant effects on reductions in energy use and energy costs, reductions in peak load, and improvement of indoor environment quality without replacing the existing equipment. Most of previous research in integrated building control just focused on optimization of specific zone or some of the possible parameters. They also applied their optimization for the current hour without considering its effect on future-hours. The main goal of this research is to develop an advanced building operation optimization tool for integrated control of lighting, shade, ventilation and heating and cooling systems for whole buildings to reduce building energy consumption, operation cost, and peak load while satisfying occupancy comfort. Also, this optimization tool is capable of coordinating integrated control and demand response by real-time modification of time-of-use prices that are received from utilities. In addition, it applies multi-hour optimization by optimizing several hours simultaneously and considering effects of current hour control parameters on future hour energy consumption. As a first step, integrated optimization is investigated based on a developed and validated RC-network model of a typical small office building. Nonlinear optimization is applied to the RC-network model that is created in MATLAB. The optimization results show energy savings up to 35% more than the scheduled control. In addition, multi-hour optimization saved up to 4% of energy cost compare to optimization based on the current hour. For more accurate building energy and cost calculation, using building simulation software is essential. In this research DOE-2 is chosen as an open source building energy use analysis tool and modified based on integrated optimization requirements by adding functions to DOE-2 source code. DOE-2 requires modifications to accept the control parameters’ online and hourly bases. Accomplished modification is validated by simulating nighttime ventilation strategy. Also, the daylighting and window energy calculation algorithm is modified to operate based on shade position instead of just open or closed shade. A building-integrated optimization tool is developed by integrating the genetic algorithm optimization method in MATLAB with building energy and cost calculation software (DOE-2). This integrated optimization tool simulates and optimizes building control parameters such as indoor temperatures, shade position, artificial light power, and outdoor air ventilation rates for an entire building. This optimization tool can be easy applied to any type of building and system when their models are available in DOE-2. Moreover, different strategies are proposed for increasing speed of optimization. First, a rule-based decision-making tool is used before integrated optimization that modifies the control parameters optimization domain. Decision-making rules are developed based on sample integrated optimization results. Second, the neural network is trained for energy consumption prediction of building based on energy consumption results from DOE-2 for random control parameters. This trained neural network is connected to a genetic algorithm and replaces DOE-2 for the energy consumption calculation. Finally, a local optimization method is used after the genetic algorithm to search around genetic algorithm results of control parameters for new control parameters with lower building energy consumption. The integrated MATLAB and DOE-2 optimization tool is initially evaluated by investigating nighttime ventilation and shade position optimization. The results for nighttime ventilation optimization show total energy savings up to 8% and cooling energy consumption reduction up to 23%. Higher savings occurred on days with high diurnal temperature range and average outdoor temperature near 17 ˚C. The results for shade position optimization indicate that in hot days shades stay nearly closed since the effect of solar heat gain, which increases cooling energy consumption in addition to the detrimental effect of conduction heat transfer, is more effective and important than lighting energy reduction from daylighting. Also, in transient seasons when the building is in heating mode, shades mostly stay open since heat gain and illuminance transmission from windows reduce both heating and lighting energy consumption. In addition, using thick shades and a lower illuminance set-point give optimization more flexibility for energy savings. Finally the integrated MATLAB and DOE-2 optimization tool for whole building energy optimization is applied to a typical office building in Montreal. The results show energy savings between 10% and 30%; also higher energy savings potential could be expected during transient seasons compared to very hot or very cold seasons. The results also show peak load savings up to 40%. Keywords: building model, energy consumption, integrated control, optimization, DOE-