Applying support vector machines to predict building energy consumption in tropical region
Applying support vector machines to predict building energy consumption in tropical region
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DOI：
10.1016/j.enbuild.2004.09.009
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发表时间：
2005-05-01
影响因子：
6.7
通讯作者：
Lee, SE
中科院分区：
文献类型：
--
作者：
Dong, B;Cao, C;Lee, SE
建筑能耗预测方法对于建筑能耗基准模型的建立和测量与验证协议（MVP）的制定越来越重要。提出了一种新的神经网络算法--支持向量机（SVM），用于热带地区建筑能耗的预测。本文的目的是检验支持向量机在建筑负荷预测领域的可行性和适用性。本文随机选取了新加坡的四栋商业建筑作为案例研究。气象数据包括月平均室外干球温度（TO），相对湿度（RH）和总太阳辐射（GSR）作为三个输入功能。每月平均房东水电费收集开发和测试模型。此外，支持向量机的性能相对于两个参数，C和e，探索使用逐步搜索方法的基础上径向基函数（RBF）核。最后，所有预测结果的变异系数（CV）小于3%，误差百分比（%误差）在4%以内。(C)2004 Elsevier B. V.保留所有权利。
The methodology to predict building energy consumption is increasingly important for building energy baseline model development and measurement and verification protocol (MVP). This paper presents support vector machines (SVM), a new neural network algorithm, to forecast building energy consumption in the tropical region. The objective of this paper is to examine the feasibility and applicability of SVM in building load forecasting area. Four commercial buildings in Singapore are selected randomly as case studies. Weather data including monthly mean outdoor dry-bulb temperature (TO), relative humidity (RH) and global solar radiation (GSR) are taken as three input features. Mean monthly landlord utility bills are collected for developing and testing models. In addition, the performance of SVM with respect to two parameters, C and e, was explored using stepwise searching method based on radial-basis function (RBF) kernel. Finally, all prediction results are found to have coefficients of variance (CV) less than 3% and percentage error (%error) within 4%. (C) 2004 Elsevier B.V. All rights reserved.