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Neural network and polynomial approximated thermal comfort models for HVAC systems

机译：HVAC系统的神经网络和多项式近似热舒适度模型

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摘要

Nowadays, the majority of people carry on their daily activities inside a building. This has motivated research directed to assure several comfort conditions. Thermal comfort is usually maintained by means of HVAC (Heating, Ventilation and Air Conditioning) systems. The most widely used thermal comfort index is the PMV (Predictive Mean Vote), which is computed considering measurements of several physical variables. The classical calculation of this index is expensive in computational terms, and the involved measurement requires a relatively extensive sensor network. This work proposes the use of two approximated models for the PMV index, one is based on an artificial neural network and the other makes use of polynomial expansions, aimed at using these approximated indices within model predictive control frameworks. In this context, the advantages of using approximated models are two-fold: the computational cost of the calculation of the index is reduced, allowing its use in real-time control of HVAC systems; and the network sensor size is decreased. These advantages entail economic benefits and promote the deployment of comfort controllers in larger structures. This paper illustrates the development of the above cited approximated models and includes experimental tests that rate the accuracy and benefits of the proposed models.
机译：如今，大多数人在建筑物内进行日常活动。这激发了旨在确保多种舒适条件的研究。通常通过HVAC（供暖，通风和空调）系统维持热舒适性。使用最广泛的热舒适指数是PMV（预测平均投票），它是通过考虑几个物理变量的测量而得出的。该指标的经典计算在计算方面是昂贵的，并且所涉及的测量需要相对广泛的传感器网络。这项工作建议使用两个近似的PMV指数模型，一个基于人工神经网络，另一个利用多项式展开，旨在在模型预测控制框架内使用这些近似指数。在这种情况下，使用近似模型的好处有两方面：降低了指数计算的计算成本，使其可用于HVAC系统的实时控制；并且网络传感器尺寸减小。这些优点带来了经济利益，并促进了舒适控制器在大型结构中的部署。本文说明了上述引用的近似模型的发展，并包括对所提出模型的准确性和收益进行评估的实验测试。

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  • 来源
    《Building and Environment》 |2013年第1期|107-115|共9页
  • 作者

    M. Castilla; J.D. Alvarez; M.G. Ortega; M.R. Arahal;

  • 作者单位

    University of Almeria Agrifood Campus of International Excellence, ceiA3, Dpto. de Lenguajes y Computacidn, Area de Ingenieria de Sistemas y Automatica, Ctra. Sacramento s, 04120 La Canada (Almeria), Spain;

    University of Sevilla, Dpto. Ingenieria de Sistemas y Autom&tica, Escuela Ticnica Superior de Ingenieria, Camino de los Descubrimientos s, 41092 Sevilla, Spain;

    University of Sevilla, Dpto. Ingenieria de Sistemas y Autom&tica, Escuela Ticnica Superior de Ingenieria, Camino de los Descubrimientos s, 41092 Sevilla, Spain;

    University of Sevilla, Dpto. Ingenieria de Sistemas y Autom&tica, Escuela Ticnica Superior de Ingenieria, Camino de los Descubrimientos s, 41092 Sevilla, Spain;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    thermal comfort; predictive mean vote; HVAC system; estimated polynomial model; artificial neural networks;

    机译：热舒适度;预测平均投票;暖通空调系统;估计多项式模型人工神经网络;

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