Journal ArticleDOI
Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network
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In this paper, the authors used a simulation-based Artificial Neural Network (ANN) to characterize building behavior, and then combined this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization.About:
This article is published in Building and Environment.The article was published on 2010-03-01. It has received 588 citations till now. The article focuses on the topics: Multi-objective optimization & TRNSYS.read more
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Journal ArticleDOI
A review on simulation-based optimization methods applied to building performance analysis
TL;DR: The review indicates that future researches should be oriented towards improving the efficiency of search techniques and approximation methods for large-scale building optimization problems; and reducing time and effort for such activities.
Journal ArticleDOI
State of the art in building modelling and energy performances prediction: A review
TL;DR: A detailed review and discussion of these works can be found in this article, where the authors present the main machine learning tools used for prediction of energy consumption, heating/cooling demand, indoor temperature.
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A review on time series forecasting techniques for building energy consumption
TL;DR: The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building and the nine most popular forecasting techniques based on the machine learning platform are analyzed.
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A decision tree method for building energy demand modeling
TL;DR: The results demonstrate that the use of decision tree method can classify and predict building energy demand levels accurately, identify and rank significant factors of building EUI automatically, and provide the combination of significant factors as well as the threshold values that will lead to high building energy performance.
Journal ArticleDOI
Algorithms for optimization of building design: A review
TL;DR: This paper is a review of the methods and tools used for the building design optimization in an effort to explore the reasoning behind their selection, to present their abilities and performance issues and to identify the key characteristics of their future versions.
References
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BookDOI
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
TL;DR: Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.
Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Journal ArticleDOI
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
Journal ArticleDOI
A comprehensive survey of fitness approximation in evolutionary computation
TL;DR: A comprehensive survey of the research on fitness approximation in evolutionary computation is presented, main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed and open questions and interesting issues in the field are discussed.