Artificial Intelligence Applied to Conceptual Design. A Review of Its Use in Architecture

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Conceptual architectural design is a complex process that draws on past experience and creativity to generate new designs. The application of artificial intelligence to this process should not be oriented toward finding a solution in a defined search space since the design requirements are not yet well defined in the conceptual stage. Instead, this process should be considered as an exploration of the requirements, as well as of possible solutions to meet those requirements. This work offers a tour of major research projects that apply artificial intelligence solutions to architectural conceptual design. We examine several approaches, but most of the work focuses on the use of evolutionary computing to perform these tasks. We note a marked increase in the number of papers in recent years, especially since 2015. Most employ evolutionary computing techniques, including cellular automata. Most initial approaches were oriented toward finding innovative and creative forms, while the latest research focuses on optimizing architectural form.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/1

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study.

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarmintelligence- based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.post-print888 K

Passive intelligent kinetic external Dynamic shade design for improving indoor comfort and minimizing energy consumption

In humid subtropical climates with a green environment, windows are the most dominant envelope elements affecting indoor visual and thermal comfort and visual connection to the outdoors. This research aims to optimize a dynamic external shading system for north-facing windows in Sydney, Australia, which acts automatically in eight predefined scenarios in response to indoor comfort conditions. The method of investigation was simulating a multi-objective optimization approach using Non-dominated Sorting Particle Swarm Optimization (NSPSO) to assess visual and thermal comfort along with energy usage and view of the outside. A combination of human and sensor assessments were applied to validate the simulations. A set of sensors and High Quality (HQ) cameras fed the system input to operate the shade. Simulations and field measurements demonstrated that optimized shading scenarios brought average yearly reductions of 71.43%, 72.52%, and 1.78% in Annual Solar Exposure, Spatial Daylight Glare, and LEED Quality View, respectively, without sacrificing Daylight Autonomy. Moreover, yearly improvements of 71.77% in cooling demand were achieved. The downside of the shading system was an increase of 0.80% in heating load and 23.76% in lighting electricity, which could be a trade-off for improved comfort and energy savings. This study investigated the effect of dynamic external shade on visual and thermal comfort together with energy usage and view, which has not been investigated for southern-hemisphere dwellings. A camera-sensor-fed mechanism operated the external shade automatically, providing indoor comfort without manual operation

The road to developing economically feasible plans for green, comfortable and energy efficient buildings

Owing to the current challenges in energy and environmental crises, improving buildings, as one of the biggest concerns and contributors to these issues, is increasingly receiving attention from the world. Due to a variety of choices and situations for improving buildings, it is important to review the building performance optimization studies to find the proper solution. In this paper, these studies are reviewed by analyzing all the different key parameters involved in the optimization process, including the considered decision variables, objective functions, constraints, and case studies, along with the software programs and optimization algorithms employed. As the core literature, 44 investigations recently published are considered and compared. The current investigation provides sufficient information for all the experts in the building sector, such as architects and mechanical engineers. It is noticed that EnergyPlus and MATLAB have been employed more than other software for building simulation and optimization, respectively. In addition, among the nine different aspects that have been optimized in the literature, energy consumption, thermal comfort, and economic benefits are the first, second, and third most optimized, having shares of 38.6%, 22.7%, and 17%, respectively

A METHODOLOGY FOR ENERGY OPTIMIZATION OF BUILDINGS CONSIDERING SIMULTANEOUSLY BUILDING ENVELOPE HVAC AND RENEWABLE SYSTEM PARAMETERS

Energy is the vital source of life and it plays a key role in development of human society. Any living creature relies on a source of energy to exist. Similarly, machines require power to operate. Starting with Industrial Revolution, the modern life clearly depends on energy. We need energy for almost everything we do in our daily life, including transportation, agriculture, telecommunication, powering industry, heating, cooling and lighting our buildings, powering electric equipment etc. Global energy requirement is set to increase due to many factors such as rapid industrialization, urbanization, population growth, and growing demand for higher living standards. There is a variety of energy resources available on our planet and non-renewable fossil fuels have been the main source of energy ever since the Industrial Revolution. Unfortunately, unsustainable consumption of energy resources and reliance on fossil fuels has led to severe problems such as energy resource scarcity, global climate change and environmental pollution. The building sector compromising homes, public buildings and businesses represent a major share of global energy and resource consumption. Therefore, while buildings provide numerous benefits to society, they also have major environmental impacts. To build and operate buildings, we consume about 40 % of global energy, 25 % of global water, and 40 % of other global resources. Moreover, buildings are involved in producing approximately one third of greenhouse gas emissions. Today, the stress put on the environment by building sector has reached dangerous levels therefore urgent measures are required to approach buildings and to minimize their negative impacts. We can design energy-efficient buildings only when we know where and why energy is needed and how it is used. Most of the energy consumed in buildings is used for heating, cooling, ventilating and lighting the indoor spaces, for sanitary water heating purposes and powering plug-in appliances required for daily life activities. Moreover, on-site renewable energy generation supports building energy efficiency by providing sustainable energy sources for the building energy needs. The production and consumption of energy carriers in buildings occur through the network of interconnected building sub-systems. A change in one energy process affects other energy processes. Thus, the overall building energy efficiency depends on the combined impact of the building with its systems interacting dynamically all among themselves, with building occupants and with outdoor conditions. Therefore, designing buildings for energy efficiency requires paying attention to complex interactions between the exterior environment and the internal conditions separated by building envelope complemented by building systems. In addition to building energy and CO2 emission performance, there are also other criteria for designers to consider for a comprehensive building design. For instance, building energy cost is one of the major cost types during building life span. Therefore, improving building efficiency not only addresses the challenges of global climate change but also high operational costs and consequent economic resource dependency. However, investments in energy efficiency measures can be costly, too. As a result, the economic viability of design options should be analysed carefully during decision-making process and cost-effective design choices needs to be identified. Furthermore, while applying measures to improve building performance, comfort conditions of occupants should not be neglected, as well. Advances in science and technologies introduced many approaches and technological products that can be benefitted in building design. However, it could be rather difficult to select what design strategies to follow and which technologies to implement among many for cost-effective energy efficiency while satisfying equally valued and beneficial objectives including comfort and environmental issues. Even using the state-of-the-art energy technologies can only have limited impact on the overall building performance if the building and system integration is not well explored. Conventional design methods, which are linear and sequential, are inadequate to address the inter-depended nature of buildings. There is a strong need today for new methods that can evaluate the overall building performance from different aspects while treating the building, its systems and surrounding as a whole and provide quantitative insight information for the designers. Therefore, in the current study, we purpose a simulation-based optimization methodology where improving building performance is taken integrally as one-problem and the interactions between building structure, HVAC equipment and building-integrated renewable energy production are simultaneously and dynamically solved through mathematical optimization techniques while looking for a balanced combination of several design options and design objectives for real-life design challenges. The objective of the methodology is to explore cost-effective energy saving options among a considered list of energy efficiency measures, which can provide comfort while limiting harmful environmental impacts in the long term therefore financial, environmental and comfort benefits are considered and assessed together. During the optimization-based search, building architectural features, building envelope features, size and type of HVAC equipment that belong to a pre-designed HVAC system and size and type of considered renewable system alternatives are explored simultaneously together for an optimal combination under given constraints. The developed optimization framework consists of three main modules: the optimizer, the simulator, and a user-created energy efficiency measures database. The responsibility of the optimizer is to control the entire process by implementing the optimization algorithm, to trigger simulation for performance calculation, to assign new values to variables, to calculate objective function, to impose constraints, and to check stopping criteria. The optimizer module is based on GenOpt optimization environment. However, a sub-module was designed, developed and added to optimization structure to enable Genopt to communicate with the user-created database module. Therefore, every time the value of a variable is updated, the technical and financial information of a matching product or system equipment is read from the database, written into simulation model, and fed to the objective formula. The simulator evaluates energy-related performance metrics and functional constraints through dynamic simulation techniques provided by EnergyPlus simulation tool. The database defines and organizes design variables and stores user-collected cost related, technical and non-technical data about the building energy efficiency measures to be tested during the optimization. An updated version of Particle Swarm Optimization with constriction coefficient is used as the optimization algorithm. The study covers multi-dimensional building design aims through a single-objective optimization approach where multi objectives are represented in a ε-Constraint penalty approach. The primary objective is taken as minimization of building global costs due to changes in design variables therefore it includes minimization of costs occur due to operational energy and water consumption together with ownership costs of building materials and building systems. Moreover, a set of penalty functions including equipment capacity, user comfort, CO2 emissions and renewable system payback period are added to the main objective function in the form of constraints to restrict the solution region to user-set design target. Consequently, multi-objective design aims are translated into a single-objective where the penalty functions acts as secondary objectives. The performance of the proposed optimization methodology was evaluated through a case study implementation where different design scenarios were created, optimized and analysed. A hypothetical base-case office building was defined. Three cities located in Turkey namely Istanbul, Ankara and Antalya were selected as building locations. Therefore, the performance of the methodology in different climatic conditions was investigated. An equipment database consists of actual building materials and system equipment commonly used in Turkish construction sector was prepared. In addition, technical and financial data necessary for objective function calculation were collected from the market. The results of the case studies showed that application of the proposed methodology achieved giving climate-appropriate design recommendations, which resulted in major cost reductions and energy savings. One of the most important contributing factors of this thesis is introducing an integrative method where building architectural elements, HVAC system equipment and renewable systems are simultaneously investigated and optimized while interactions between building and systems are being dynamically captured. Moreover, this research is distinctive from previous studies because it makes possible investigating actual market products as energy efficiency design options through its proposed database application and a sub-program that connect optimization engine with the data library. Therefore, application of the methodology can provide support on real-world building design projects and can prevent a mismatch between the optimization recommendations and the available market solutions. Furthermore, another contributing merit of this research is that it achieves formulating competing building design aims in a single objective function, which can still capture multi-dimensions of building design challenge. Global costs are minimized while energy savings are achieved, CO2-equivalent emission is reduced, right-sized equipment are selected, thermal comfort is provided to users and target payback periods of investments are assured. To conclude, the proposed methodology links building energy performance requirements to financial and environmental targets and it provides a promising structure for addressing real life building design challenges through fast and efficient optimization techniques

Determination of building shape for energy efficiency using simulation and particle swarm optimization

The building envelope shape is the most salient design characteristic and has a significant influence on energy consumption during the post-occupancy service life. However, during the conceptual design phase, envelope shape-finding is defined without considering post-occupancy service life energy performance. This warranted absence of a priori knowledge on shape-based convective heat transfer affects indoor environment quality and impedes the ability to meet post-occupancy energy performance efficiency requirements. In addition, there is no suitable method for designers by which to make such calculations. In an attempt to optimize energy consumption and reduce the post-occupancy service life in efficiency, this research aims to determine building shape energy efficiency using a simulation and optimization process that can facilitate the designer’s task during the conceptual design phase. For this purpose, a case study research method and simulation-based particle swarm optimization process was conducted. Foremost, it is pertinent to understand building shape behavior in order to improve energy efficiency. For this, a longitudinal case study set out to collect real time energy data and historical building data by a selected unit of analysis Block C 02, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia. The building shapes were simulated using thermal transient simulation for heat transfer analysis. However, results indicated that a proportionate increase in building shape compactness, aspect ratio or coefficient can adversely affect building shape thermal performance, affirming the proposition that convective heat transfer and solar radiation have a considerable influence on energy consumption based on shape geometrical characteristics. Following this, a varied combination of shapes, wall window ratio and glazing energy performance was then analyzed using particle swarm optimization to determine the optimal envelope shape combination. The results confirmed that, as the shape achieves its geometric efficiency, it appropriates the wall window ratio and glazing proportions that reduce convective heat transfer. A design approach that can determine shape energy efficiency based on simulation and particle swarm optimization was then developed. Further, sensitivity of this design approach was calibrated using comparative testing and empirical validation. The findings provide a benchmark of energy consumption based on a combination of envelope shape characteristics, wall window ratio and glazing. In conclusion, this research has succeeded in transforming the conventional shape-finding process into an integrated simulation-based shape optimization for energy efficiency. The major contribution of this research study was that it developed a design approach for building shape energy efficiency and optimization. It can facilitate the task of designers during the conceptual design phase by disposing of their one-off design solutions and making it feasible to conceptualize varied building shapes for energy efficient design solutions

Energy in Dwellings

Energy simulation models for buildings are widely used by policymakers, researchers and consultants as a tool to advice on the reduction of residential energy consumption. Previous studies have shown that there is a gap between theoretical building energy simulation results and actual energy use. The discrepancy between theory and practice is problematic, as for instance expected energy savings are often not achieved. This thesis shows that analysing specific household types and building characteristics can contribute to a better understanding of amongst others the influence of the occupant on actual energy consumption. The effectiveness of thermal renovations is dependent on both occupants and building characteristics, which means tailored advice on renovation measures is necessary. We also found that occupants and building characteristics are equally responsible for variances in actual residential energy consumption. To reduce the gap between theory and practice on a single building level, simulation models are improved using calibration methods. In the final part of this thesis, a method is developed to calibrate simulations on a building stock level, making building energy simulation tools more reliable for policymakers

Review of Intelligent Control Systems for Natural Ventilation as Passive Cooling Strategy for UK Buildings and Similar Climatic Conditions

Natural ventilation is gaining more attention from architects and engineers as an alternative way of cooling and ventilating indoor spaces. Based on building types, it could save between 13 and 40% of the building cooling energy use. However, this needs to be implemented and operated with a well-designed and integrated control system to avoid triggering discomfort for occupants. This paper seeks to review, discuss, and contribute to existing knowledge on the application of control systems and optimisation theories of naturally ventilated buildings to produce the best performance. The study finally presents an outstanding theoretical context and practical implementation for researchers seeking to explore the use of intelligent controls for optimal output in the pursuit to help solve intricate control problems in the building industry and suggests advanced control systems such as fuzzy logic control as an effective control strategy for an integrated control of ventilation, heating and cooling systems