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首页> 外文期刊>Electric Power Components and Systems >Improved Method for Approximation of Heating and Cooling Load in Urban Buildings for Energy Performance Enhancement
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Improved Method for Approximation of Heating and Cooling Load in Urban Buildings for Energy Performance Enhancement

机译：城市建筑中加热和冷却负荷近似的改进方法，实现能源性能增强

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摘要

Estimation of a building's heating and cooling loads is an important factor taken into account implementation of energy saving measures in order to enhance energy performance of the building. In this work, the heating and cooling loads are predicted to enhance the building energy performance using different types of artificial neural networks namely, Elman network, recurrent network and back propagation network. The effect of eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) on two output variables (heating load and cooling load of residential buildings) is studied. The collected features are given as input to various neural networks for predicting the heating and cooling loads. The performance of the method is calculated in terms of mean absolute error, mean square error and mean relative error. Among all the networks back-propagation neural network has highest accuracy. The mean absolute error in predicting the loads is found to be 0.1 for heating load and 0.1254 for cooling load which is much better than already existing methods. The results of the work further reinforce the fact that ANN is an important tool for prediction and analysis of energy performance of a building.
机译：建筑物的加热和冷却负荷估计是考虑到节能措施的重要因素，以提高建筑物的能量性能。在这项工作中，预测加热和冷却载荷，以利用不同类型的人工神经网络来增强建筑能量性能即，Elman网络，经常性网络和后传播网络。研究了八个输入变量（相对紧凑性，表面积，壁面积，屋顶区域，整体高度，方向，定向，玻璃面积分布）在两个输出变量（居住建筑物的加热负荷和冷却负荷）的影响。收集的特征被给予各种神经网络的输入，用于预测加热和冷却负载。根据平均绝对误差，均方误差和平均相对误差计算该方法的性能。在所有网络中，反向传播神经网络具有最高的精度。预测负载的平均绝对误差是加热负载的0.1，用于冷却负荷为0.1254，这比已经存在的方法好得多。该工作的结果进一步加强了ANN是建筑物能量性能预测和分析的重要工具。

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