在运行阶段利用人工神经网络模型和机器学习进行建筑生命周期管理

L. Suleymanova, A. Obaydi
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引用次数: 0

摘要

使用人工神经网络和机器学习方法分析建筑物的热损失对现代建筑具有重要意义。这些技术在数据处理方面精度高、效率高。人工神经网络能够分析海量信息并识别复杂的模式,从而显著提高确定建筑物热损失的准确性。反过来,机器学习方法可以考虑各种影响因素,如地理位置和气象条件,从而为提高分析结果的质量做出重大贡献。这些方法可以提供更可靠、更准确的结论,这对于有效的能源管理和减少建筑物的热损失至关重要。在本文中,作者利用人工神经网络和机器学习方法对建筑物的热损失及其在运行阶段的预测进行了研究。该技术基于对热损失数据及其与各种建筑参数关系的分析。使用 Statistica 软件包中的人工神经网络和基于 scikit-learn 库的机器学习方法进行了预测。所提出的方法可以有效地管理建筑物的能源消耗,优化能源效率,改善基本建设项目的生命周期管理。研究结果表明,该模型具有较高的准确性和与实际值的收敛性,同时还具有预测性能的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BUILDING LIFE CYCLE MANAGEMENT AT THE OPERATION STAGE USING ARTIFICIAL NEURAL NETWORK MODELS AND MACHINE LEARNING
The use of artificial neural networks and machine learning methods for the analysis of heat loss in buildings is of significant relevance in modern construction. These technologies are highly accurate and efficient in data processing. Artificial neural networks have the ability to analyze vast amounts of information and identify complex patterns, which significantly increases the accuracy of determining heat loss in buildings. In turn, machine learning methods make it possible to take into account various influencing factors, such as geographic location and meteorological conditions, thereby making a significant contribution to improving the quality of analytical results. Such approaches provide more reliable and accurate conclusions, which is critical for effective energy management and reducing heat loss in buildings. In this article, the authors conducted a study of heat losses of buildings and their prediction at the operational stage using artificial neural networks and machine learning methods. The technique is based on the analysis of data on heat loss and their relationship with various building parameters. Forecasting was carried out using artificial neural networks in the Statistica software package and the machine learning method based on the scikit-learn library. The proposed approach allows you to effectively manage the energy consumption of a building, optimizing its energy efficiency and improving the life cycle management of a capital construction project. The results demonstrate the high accuracy and convergence of the model with actual values, as well as its ability to predict performance.
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