能源效率数据集，12种不同建筑物形状进行能量分析

Data Set Information: We perform energy analysis using 12 different building shapes simulated in Ecotect. The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valued responses. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer. Attribute Information: The dataset contains eight attributes (or features, denoted by X1...X8) and two responses (or outcomes, denoted by y1 and y2). The aim is to use the eight features to predict each of the two responses. Specifically: X1 Relative Compactness X2 Surface Area X3 Wall Area X4 Roof Area X5 Overall Height X6 Orientation X7 Glazing Area X8 Glazing Area Distribution y1 Heating Load y2 Cooling Load Relevant Papers: A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012 Citation Request: A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012 (the paper can be accessed from [Web link]) For further details on the data analysis methodology: A. Tsanas, 'Accurate telemonitoring of Parkinsona€?s disease symptom severity using nonlinear speech signal processing and statistical machine learning', D.Phil. thesis, University of Oxford, 2012 (which can be accessed from [Web link]) The dataset was created by Angeliki Xifara (angxifara '@' gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis '@' gmail.com, Oxford Centre for Industrial and Applied Mathematics, University of Oxford, UK).

数据集信息：我们采用Ecotect软件模拟的12种不同建筑形体开展能耗分析。这些建筑在玻璃面积、玻璃面积分布、朝向及其他多项参数上存在差异。我们以上述特征为变量设置多种模拟工况，最终得到768组建筑形体样本。该数据集包含768个样本与8个特征，目标为预测两个实值输出结果；若将输出结果四舍五入至整数，则也可将其作为多分类任务开展研究。
属性信息：本数据集包含8个属性（或称特征，记为X1至X8）与2个输出变量（或称结果，记为y1与y2），任务为利用8个特征分别预测这两个输出结果。具体如下：
X1 相对紧凑度（Relative Compactness）
X2 表面积（Surface Area）
X3 墙体面积（Wall Area）
X4 屋顶面积（Roof Area）
X5 总高度（Overall Height）
X6 朝向（Orientation）
X7 玻璃面积（Glazing Area）
X8 玻璃面积分布（Glazing Area Distribution）
y1 采暖负荷（Heating Load）
y2 制冷负荷（Cooling Load）
相关文献：A. Tsanas、A. Xifara：《利用统计机器学习工具精准量化估算住宅建筑能耗性能》，《能源与建筑（Energy and Buildings）》，第49卷，第560-567页，2012年
引用要求：A. Tsanas、A. Xifara：《利用统计机器学习工具精准量化估算住宅建筑能耗性能》，《能源与建筑（Energy and Buildings）》，第49卷，第560-567页，2012年（该文献可通过[Web link]获取）。如需了解更多数据分析方法论细节，请参阅A. Tsanas的博士论文：《利用非线性语音信号处理与统计机器学习技术精准远程监测帕金森病症状严重程度》，牛津大学哲学博士（D.Phil.）学位论文，2012年（可通过[Web link]获取）。
本数据集由Angeliki Xifara（土木/结构工程师，邮箱：[e-mail]）创建，由Athanasios Tsanas（英国牛津大学工业与应用数学中心，邮箱：[e-mail]）完成数据处理。