2016
DOI: 10.1016/j.egypro.2015.12.279
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The Influence of Genetic Algorithm Parameters Over the Efficiency of the Energy Consumption Estimation in a Low–energy Building

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Cited by 12 publications
(5 citation statements)
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“…Instead, multiple solutions constitute an optimal solution set. This solution set is called the Pareto solution set (Mihail-Bogdan et al, 2016) and appears as the tangent points of all subobjective functions in mathematical image.…”
Section: Optimization Of Energy-saving Parameters Based On Genetic Algorithmmentioning
confidence: 99%
“…Instead, multiple solutions constitute an optimal solution set. This solution set is called the Pareto solution set (Mihail-Bogdan et al, 2016) and appears as the tangent points of all subobjective functions in mathematical image.…”
Section: Optimization Of Energy-saving Parameters Based On Genetic Algorithmmentioning
confidence: 99%
“…142 6.1 The description of the sensors by which the data have been measured 154 6.2 Calibration correlations for correction of the measured air temperatures 156 6.3 R 2 values in comparison between the parameters in two locations and their average values and the in-situ measured values. December 2018-February 2019 158 6.4 Total, SH-related and DHW-related gas consumption in different periods 162 6.5 Upper and lower bounds of the parameters defined in the optimization problem 171 List of Tables TOC ListofTables 6.6 Results of the optimization for different period lengths using different granularity levels 173 6.7 Results of the optimization for November 2017 and January 2018 using different granularity levels 173 6.8 The building's components and their properties 176 6.9 Indoor and outdoor assumed average convective heat transfer coefficients 177 The output of the model is the heating energy demand. 38 1.2 Theory of RFs: generation of RFs X and Y as a response to signals T 1 and T 2 45 2.1 General configuration of ISO 9869 standard measurement with one extra HFS added.…”
Section: Comparison Between the Rc-values By Isomentioning
confidence: 99%
“…They present the method to simplify energy models using easily available and short-period data. The GA has been often used as a promising optimization technique for the buildings' inverse modelling problems [162]. Costola et al [149] used GA to optimize 34 parameters of their model, fed with smart meter energy data, to show the capability of this method in making reliable estimations.…”
Section: Inverse Modelling At Building Levelmentioning
confidence: 99%
“…Development of a helpful method for setting the appropriate algorithm parameters is one of the most demanding and important areas of research in BEO especially for expensive computational optimization problems. Few works have examined this topic [23][24][25]. In this study, we do not focus on this topic.…”
Section: Literature Reviewmentioning
confidence: 99%