Optimization of Trusses With Self-Adaptive Approach In Genetic Algorithms

2018

Sensors

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An inappropriate indoor climate, mostly indoor temperature, may cause occupants’ discomfort. There are a great number of air conditioning systems that make it possible to maintain the required thermal comfort. Their installation, however, involves high investment costs and high energy demand. The study analyses the possibilities of limiting too high a temperature in residential buildings using passive cooling by means of ventilation with ambient cool air. A fuzzy logic controller whose aim is to control mechanical ventilation has been proposed and optimized. In order to optimize the controller, the modified Multiobjective Evolutionary Algorithm, based on the Strength Pareto Evolutionary Algorithm, has been adopted. The optimization algorithm has been implemented in MATLAB®, which is coupled by MLE+ with EnergyPlus for performing dynamic co-simulation between the programs. The example of a single detached building shows that the occupants’ thermal comfort in a transitional climate may improve significantly owing to mechanical ventilation controlled by the suggested fuzzy logic controller. When the system is connected to the traditional cooling system, it may further bring about a decrease in cooling demand.

Section: Methodsmentioning

confidence: 99%

Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

2018

Sensors

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“…SGA was applied simultaneously in the selection of chromosomes and to control basic parameters of genetic algorithm. More information is presented in papers by Grygierek [31,32,33]. SGA and all procedures to exchange data between simulation and optimization tool were implemented in MATLAB language.…”

Section: Methods and Simulation Modelmentioning

confidence: 99%

Optimization of Window Size Design for Detached House Using Trnsys Simulations and Genetic Algorithm

Architecture, Civil Engineering, Environment

2017

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Heat gains from the sun affect the heat balance of building by reducing the energy demand at certain periods of the year and increasing it at others. Windows, especially the type of glazing, are a determining factor in the successful use of solar gains. The aim of the research presented in the paper is to analyse the effects of the type and size of windows on annual heating and cooling energy consumption considering the energy costs in Polish climate conditions. Additionally the influence of building orientation has been analysed. Optimal selection of these parameters for reduction of the energy consumption has been carried out. Genetic algorithms were used for the optimization, while TRNSYS program was used for energy analysis. The analyses were performed on an exemplary single family detached house. Self-adaptive genetic algorithm connected with energy building simulation successfully identifies the lowest energy costs. Optimal window type and size design and window orientation reduce the energy costs. The developed comprehensive energy simulation environment can also be used to optimize other building's parameters.

“…GAs are the most popular methods, which have found vast amount of applications in a wide spectrum of diverse engineering disciplines [2,18,19,46], including structural and energy optimization. The fundamentals of this algorithm are outlined in the work of Goldberg [47] in 1989.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Multi-Variable Optimization Models for Building Envelope Design Using Energyplus Simulation and Metaheuristic Algorithms

Architecture, Civil Engineering, Environment

2019

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The paper presents the method of optimal design of the building envelope. The influence of four types of windows, their size, building orientation, insulation of external walls, ceiling to unheated attic and ground floor on the life cycle costs in a single-family building in Polish climate conditions is analyzed. The optimization procedure is developed by means of the coupling between MATLAB and EnergyPlus. The results using three metaheuristic methods: genetic algorithms, particle swarm optimization, and algorithm based on teaching and learning are compared. The analyses have shown the possibility of reducing the life cycle costs through the optimal selection of the building structure. The high initial investment (above the required standard) pays off in the long run when using a building.