Journal
ENERGY AND BUILDINGS
Volume 88, Issue -, Pages 135-143Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2014.11.063
Keywords
Building design; Energy consumption; Thermal comfort; Artificial neural network; Multi-objective genetic algorithm
Funding
- National Key Technologies R&D Program of China Project [2013BAJ03B05]
- National Natural Science Foundation of China [51408079]
- State Key Laboratory of Building Safety and Built Environment [B13041]
- 111 Project [B13041]
- Fundamental Research Funds for the Central Universities [2013CDJZR210008]
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Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA-BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA-BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. (C) 2014 Elsevier B.V. All rights reserved.
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