建筑能耗预测的人工神经网络模型

K. Ahn, Cheol-Soo Park
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引用次数: 3

摘要

全国需要一个快速简便的现有建筑能源绩效评估系统,而不需要使用动态的建筑能源模拟工具,这通常需要大量的成本、时间和专业知识。在本研究中,作者报告了一种基于人工神经网络(ANN)模型的建筑能耗分析系统的开发。人工神经网络模型是通过一系列EnergyPlus预模拟,通过蒙特卡罗技术采样得到的。EnergyPlus模型与ANN模型的MBE和CVRMSE分别为1.53%和7.82%。结论是,剖面系统需要最少的输入,并为给定的建筑提供准确的能源性能评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial neural network models for building energy prediction
There is a national need for a quick and easy building energy performance assessment system of existing buildings, without resorting to dynamic building energy simulation tools which usually require significant cost, time and expertise. In this study, the authors report the development of a building energy profiling system which is based on Artificial Neural Network (ANN) models. The ANN models were made by a series of EnergyPlus pre-simulations sampled by a Monte Carlo technique. The MBE and CVRMSE between EnergyPlus and ANN models are 1.53% and 7.82%, respectively. It is concluded that the profiling system requires minimalistic inputs and provides accurate energy performance assessment of a given building.
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