Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances

Evolving Passive Solar Buildings Using Multi-Behavioural Diversity Search Strategies

To build a green environment and to plan a sustainable urban area, energy efficient building design plays a major role. Energy efficient measures for building design include heating, cooling, and ventilating, as well as construction materials cost. In passive solar building design, sunlight exposure is used to heat the building in winter and reject heat in summer to keep the building cool. The goals of the passive solar building design are to minimize the energy cost and devices used for heating or cooling. The major goal of this research is to increase the diversity of solutions evolved with an evolutionary system for green building design. An existing genetic programming system for building design is enhanced with a search paradigm called novelty search, which uses measured aspects of designs in an attempt to promote more diverse or novel solutions. Instead of optimizing an objective, novelty search measures behaviors to obtain diverse solutions. We combine novelty search and fitness scores using a many objective strategy called sum of ranks. The simulation software EnergyPlus is used to evaluate the building design and energy costs. An existing fitness-based genetic programming system is enhanced with novelty search. We compare vanilla genetic programming solutions with our novelty-driven solutions. Experimental results show that genetic program solutions are more fit, but novelty strategies create more diverse solutions. For example, novelty search solutions, use a much more diverse selection of building materials

Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table

Simulation-based optimization of thermal energy storage (TES) materials for building and industry applications

Una quantitat substancial d'energia s'utilitza en els sectors de l'edificació i de la indústria per als propòsits de la calefacció i de la refrigeració. Els materials d'emmagatzematge d'energia tèrmica (TES) poden oferir importants beneficis energètics i econòmics als edificis residencials, comercials i industrials. Els materials de TES tenen el potencial per reduir les demandes de refredament i de l'electricitat màxima en sectors de l'edificació i de la indústria; però, per tal d'implementar adequadament aquesta tecnologia per maximitzar els beneficis econòmics, es necessiten tècniques de simulació i optimització numèrica. La important contribució original que emergeix de la present tesi és l'ús de mètodes de simulació numèrica i optimització per avançar l'aplicació de la tecnologia TES en els sectors residencials i industrials. Per a això, es presentarà una revisió pel que fa a l'ús d'eines de simulació d'energia per al desenvolupament d'edificis per analitzar passivament els materials realçats amb TES.Una cantidad substancial de energía se utiliza en los sectores de la edificación y de la industria para los propósitos de la calefacción y de la refrigeración. Los materiales de almacenamiento de energía térmica (TES) pueden ofrecer importantes beneficios energéticos y económicos a los edificios residenciales, comerciales e industriales. Los materiales de TES tienen el potencial para reducir las demandas de enfriamiento y de la electricidad máxima en sectores de la edificación y de la industria; sin embargo, con el fin de implementar adecuadamente esta tecnología para maximizar los beneficios económicos, se necesitan técnicas de simulación y optimización numérica. La importante contribución original que emerge de la presente tesis es el uso de métodos de simulación numérica y optimización para avanzar la aplicación de la tecnología TES en los sectores residenciales e industriales.A substantial amount of energy is used in building and industry sectors for heating and cooling purposes. Thermal energy storage (TES) materials can offer important short-term and long-term energy, economic, and comfort benefits to residential, commercial, and industrial buildings. TES materials have the potential to reduce the cooling and peak electricity demands in building and industry sectors, however, in order to properly implement this technology to maximize the economic benefits, numerical simulation and optimization techniques are necessary. The significant original contribution emerges from the present thesis is the use of numerical simulation and optimization methods to advance the application of TES technology in the industrial and building sector. To achieve this, a review will be presented regarding the use of whole-building energy simulation tools to analyse buildings passively enhanced with TES materials

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimization tools in net zero energy buildings (NZEB) design. The paper reviews current trends in simulation-based building performance optimization (BPO) and outlines major criteria for optimization tools selection and evaluation. This is based on analyzing user's needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimization experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB design. The findings indicate a breakthrough in using evolutionary algorithms in solving highly constrained envelope, HVAC and renewable optimization problems. Simple genetic algorithm solved many design and operation problems and allowed measuring the improvement in the optimality of a solution against a base case. Evolutionary algorithms are also easily adapted to enable them to solve a particular optimization problem more effectively. However, existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented.Peer reviewe

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Multi-objective optimization of the solar orientation of two residential multifamily buildings in south Brazil

The shape and orientation of a building influence the energy demand, therefore optimal decisions should only be made rigorously supported by energy evaluation programs, which allow for measuring the energy demand of a building more precisely. The main purpose of this research is to evaluate the shape and orientation of massive residential social housing multifamily buildings to find the best solar positioning to minimize cooling and heating demands simultaneously in the bioclimatic zone 2 (Cfa) in the southern region of Brazil. To do this, this study utilizes multi-objective optimization with a genetic algorithm (NSGA-II) simulating the thermal behavior in EnergyPlus and performing the optimization with a Python language programming code, totalizing 80,000 simulations. The main results showed that solar orientation optimization could reduce the total demand by 4% for the ‘‘H” shape and 22% for linear buildings in the isolated scenario. For the condominium condition, the reduction is 2% for the ‘‘H” typology and 8% for the linear shape. The results presented can help engineers and architects to design more energyefficient buildings and address the energetic vulnerability in the same building. Moreover, future work can be carried out to improve the constructive pattern replicated all over the country, improving the surroundings

Multi-objective optimization of the solar orientation of two residential multifamily buildings in south Brazil

The shape and orientation of a building influence the energy demand, therefore optimal decisions should only be made rigorously supported by energy evaluation programs, which allow for measuring the energy demand of a building more precisely. The main purpose of this research is to evaluate the shape and orientation of massive residential social housing multifamily buildings to find the best solar positioning to minimize cooling and heating demands simultaneously in the bioclimatic zone 2 (Cfa) in the southern region of Brazil. To do this, this study utilizes multi-objective optimization with a genetic algorithm (NSGA-II) simulating the thermal behavior in EnergyPlus and performing the optimization with a Python language programming code, totalizing 80,000 simulations. The main results showed that solar orientation optimization could reduce the total demand by 4% for the “H” shape and 22% for linear buildings in the isolated scenario. For the condominium condition, the reduction is 2% for the “H” typology and 8% for the linear shape. The results presented can help engineers and architects to design more energy-efficient buildings and address the energetic vulnerability in the same building. Moreover, future work can be carried out to improve the constructive pattern replicated all over the country, improving the surroundings.Peer ReviewedPostprint (published version