Performance indices and evaluation of algorithms in building energy efficient design optimization

Si, Binghui; Tian, Zhichao; Jin, Xing; Zhou, Xia; Tang, Peng; Shi, Xing

doi:10.1016/j.energy.2016.07.114

Cited by 62 publications

(27 citation statements)

References 21 publications

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“…They analyzed the characteristics of design elements, such as the direction, area ratio, and wall conductivity, which are passive elements, and then performed optimization to obtain values that minimized energy consumption. Si et al [15] determined the optimal design for the positions of windows, direction of the building, and thermal conductivity using pattern search algorithms, genetic algorithms, and particle swarm optimization (PSO) algorithms to design energy-efficient buildings. Many studies on optimization in the design stages of buildings have been conducted using various optimization algorithms, but most of them were focused on the optimization of passive elements of buildings to minimize energy consumption.…”

Section: Research Backgroundmentioning

confidence: 99%

Study on the Optimum Design of a Ground Heat Pump System Using Optimization Algorithms

2019

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Geothermal energy has attracted attention as a high-efficiency energy source that can be used year-round, but it has a relatively higher initial investment cost. For the design of ground source heat pump (GSHP) systems, a calculation method to determine the capacity of a system to meet the peak load of the target building is usually used. However, this method requires excessive system capacity design, especially regarding buildings with partial load operations. In this study, the optimization of a system design was performed in the view of the cost of the lifecycle cost. Several optimization algorithms were considered, such as the discrete Armijo gradient algorithm, a particle swarm optimization (PSO) algorithm, and a coordinate search method algorithm. The results of the optimization described the system capacity (heat pump, ground heat exchanger, thermal storage tank, etc.) and the cost performance, showing that the total investment cost was reduced compared to the existing design.

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Section: Research Backgroundmentioning

confidence: 99%

Study on the Optimum Design of a Ground Heat Pump System Using Optimization Algorithms

2019

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“…• Exclusively focus on the algorithms' performance in solving BEO problems. Based on our recently published review [2], only a few researchers have paid close attention to such topics [4,17,18]. However, in this sector, research studies that focus on the properties of BEO problems are seriously scarce.…”

Section: Literature Reviewmentioning

confidence: 99%

“…automatically adjust designs, has emerged [3]. It combines optimization techniques with building energy simulation tools and relies on optimization algorithms to create new designs according to pre-defined optimization objectives and simulation results [4]. Thus, optimization algorithms are the key to the BEO workflow, and the effectiveness and efficiency of this technique significantly depend on the performance of the algorithms.There are various algorithms for solving optimization problems in many science and engineering fields.…”

mentioning

confidence: 99%

Performance Assessment of Algorithms for Building Energy Optimization Problems with Different Properties

Tian

Chen

et al. 2018

Sustainability

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Assessing the performance of algorithms in solving building energy optimization (BEO) problems with different properties is essential for selecting appropriate algorithms to achieve the best design solution. This study begins with a classification of the properties of BEO problems from three perspectives, namely, design variables, objective functions, and constraints. An analytical approach and a numerical approach are proposed to determine the properties of BEO problems. Six BEO test problems with different properties, namely, continuous vs. discrete, convex vs. non-convex, linear vs. non-linear, uni-modal vs. multimodal, and single-dimensional vs. multi-dimensional, are composed to evaluate the performance of algorithms. The selected optimization algorithms for performance assessment include the discrete Armijo gradient, Particle Swarm Optimization (PSO), Hooke-Jeeves, and hybrid PSO and Hooke-Jeeves. The assessment results indicate that multimodality can cause Hooke-Jeeves and discrete Armijo gradient algorithms to fall into local optima traps. The convex, non-convex, linear and non-linear properties of uni-modal BEO problems have little impact on the performance behavior of the algorithms. The discrete Armijo gradient and Hooke-Jeeves are not recommended for solving discrete and multi-dimensional BEO problems. automatically adjust designs, has emerged [3]. It combines optimization techniques with building energy simulation tools and relies on optimization algorithms to create new designs according to pre-defined optimization objectives and simulation results [4]. Thus, optimization algorithms are the key to the BEO workflow, and the effectiveness and efficiency of this technique significantly depend on the performance of the algorithms.There are various algorithms for solving optimization problems in many science and engineering fields. The optimization algorithms commonly used in BEO can be generally divided into three groups, namely, hybrid algorithms, direct search algorithms and heuristic algorithms [5]. As shown in some reviews [6][7][8], evolutionary algorithms are the most popular algorithms, accounting for about 60%. The genetic algorithm (GA) [9] and its variations such as the non-dominated sorting genetic algorithm II (NSGA-II) [10] are typical examples of evolutionary algorithms. The Hooke-Jeeves algorithm [11] is a representative of direct search algorithms. A hybrid algorithm [12] is an algorithm that combines two or more other algorithms into a hybrid operation so that the overall algorithm performs better than the individual ones.Although a number of algorithms are currently used in BEO, their performance, i.e., in terms of effectiveness and efficiency, can vary and at times significantly. The reasons for the difference in performance of algorithms are two-fold. First, BEO problems have different properties, e.g., linear or non-linear, single-dimensional or multi-dimensional, uni-modal or multimodal, etc. Second, the performance of a particular algorithm is closely linked with the properties of...

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“…Derivative-free search are the direct search methods [23] like Hooke-Jeeves, coordinate search and mesh adaptive search, among others. The hybrid is an integration of various methods [22]. One of the combined methods often times serves to offset the bias of the other.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…[21]; these are evolutionary, derivative-free search and the hybrid algorithms. Evolutionary methods include genetic algorithms (GAs) and its improved states like non-dominated sorting genetic algorithm II (NSGA-II) among the rest, particle swarm optimization (PSO) and other evolutionary methods [22]. Derivative-free search are the direct search methods [23] like Hooke-Jeeves, coordinate search and mesh adaptive search, among others.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Assessing the Possible Potential in Energy Consumption and Greenhouse Gas Emission: Application of a Proven Hybrid Method

Olanrewaju¹,

Mbohwa²

2017

Research and Development Evolving Trends and Practices - Towards Human, Institutional and Economic Sectors Growth

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Energy evaluation together with greenhouse gas mitigation goes a long way in sustaining the growth and economy of a nation. Various evaluation methods have been adopted by researchers, academia and various country wise energy department ministries to achieve this aim. The most effective method is the hybrid evaluation method. This takes into consideration strength of a particular method to overcome the weakness of another method. This chapter focuses on a recently proven integrated method on energy and greenhouse gas studies-integrated IDA-ANN-DEA (index decomposition analysis-artificial neural network-data envelopment analysis). Case studies were exemplified using this approach in evaluating possible energy potential that could be saved in the manufacturing industries in Canada and South Africa as well as a particular food and beverage industry. Another case study focused on the amount of possible greenhouse gas that could be mitigated in the Canadian industry. The hybrid model proved very useful in its analysis. Keywords: energy, evaluation, greenhouse gas, mitigation, hybrid method, IDA-ANN-DEA Overview Energy's involvement in the growth of the economy is very crucial. The role it plays, as well as greenhouse gas, has led to various questions leading to various practices and trends in research and development (R&D). One of the leading objectives of R&D in the energy sector is to find how best to conserve energy. To achieve this objective, research trends in energy use, energy potentials, policy formulations as well as investment patterns and technology choices have been ongoing.

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Performance indices and evaluation of algorithms in building energy efficient design optimization

Cited by 62 publications

References 21 publications

Study on the Optimum Design of a Ground Heat Pump System Using Optimization Algorithms

Study on the Optimum Design of a Ground Heat Pump System Using Optimization Algorithms

Performance Assessment of Algorithms for Building Energy Optimization Problems with Different Properties

Assessing the Possible Potential in Energy Consumption and Greenhouse Gas Emission: Application of a Proven Hybrid Method

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