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A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes

机译：一种基于极限学习机的新方法，通过设计和结构属性来预测加热和冷却负荷

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摘要

In the present day environment, smart buildings require optimization of energy consumption through monitoring, consumption prediction and making policy decisions accordingly. Attributes related to building design and structure play a vital role in heating load(HL) and cooling load(CL) of the building which directly affects the energy performance of the buildings. For prediction of HL and CL, emerging machine learning approaches can help in improving accuracy and efficiency in real time. This paper provides improvements in energy load assessment of the buildings. It is the first is the in-depth study and analysis of design and structural attributes and their correlation with HL and CL, the novel methods based on ELM and its variants online sequential ELM(OSELM) to predict HL and CL. This study also proposes OSELM based online/real-time prediction when data is coming in stream The total 24 models have been developed including 12 models based on ELM and 12 models based on OSELM with different feature sets and activation functions. Models have been compared on the basis of accuracy, computational performance and efficiency with few existing models. The experimental results show that the proposed models learn better and outperform other popular machine learning approaches such as the artificial neural network(ANNs), support vector machine(SVM), radial basis function network(RBFN), random forest(RF) and exiting work in the energy and building domain. (C) 2018 Published by Elsevier B.V.
机译：在当今的环境中，智能建筑需要通过监控，能耗预测以及相应地制定政策决策来优化能耗。与建筑物设计和结构有关的属性在建筑物的热负荷（HL）和制冷负荷（CL）中起着至关重要的作用，直接影响建筑物的能源性能。为了预测HL和CL，新兴的机器学习方法可以帮助实时提高准确性和效率。本文对建筑物的能量负荷评估进行了改进。这是对设计和结构属性及其与HL和CL的相关性的深入研究和分析，这是基于ELM及其变体在线顺序ELM（OSELM）预测HL和CL的新方法。这项研究还提出了当数据流入时基于OSELM的在线/实时预测。已经开发了总共24个模型，其中包括12个基于ELM的模型和12个基于OSELM的具有不同功能集和激活功能的模型。已经在准确性，计算性能和效率的基础上对模型进行了比较，而现有模型很少。实验结果表明，所提出的模型能够更好地学习，并且胜过其他流行的机器学习方法，例如人工神经网络（ANN），支持向量机（SVM），径向基函数网络（RBFN），随机森林（RF）和现有工作在能源和建筑领域。 （C）2018由Elsevier B.V.发布

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