准确的建筑能耗预测模型是先进建筑能耗系统实时控制和建筑-电网一体化的关键。特征选择是选择相关特征子集的过程，在数据驱动建模中是一个必不可少的过程，因为它能够降低模型的复杂性，增加模型的可解释性，增强模型的泛化能力。在建筑能耗建模研究中，特征的选择往往完全基于领域知识。在开发建筑能耗预测模型时，缺乏全面的方法来指导特征选择过程。本研究结合统计数据分析、建筑物理和工程实践，提出了一种开发建筑能耗预测模型的系统特征选择方法。该过程包括三个主要步骤:（1）基于领域知识的基于规则的特征预选过程。 （2）特征去除过程通过过滤方法去除无关和冗余变量。以及（步骤3）使用包装器方法获得最佳特征组合。本文使用从中型商业建筑（能源部参考建筑）生成的模拟建筑能源数据进行了案例研究。在这项研究中，使用所提出的系统特征选择过程生成的能源预测模型与其他模型进行了比较，例如使用常规输入的模型和使用单一特征选择技术的模型。比较结果表明，在交叉验证误差方面，采用系统特征选择过程的模型比包括常规输入和仅使用单一特征选择技术的其他模型表现出更好的模型性能。引用:2017年年度会议，加利福尼亚州长滩，会议论文

An accurate building energy forecasting model is the key for real-time control of advanced building energy system and building-to-grid integration. Feature selection, the process of selecting a subset of relevant features, is an essential procedure in data-driven modeling due to its ability to reduce model complexity, increase model interpretability, and enhance model generalization. In building energy modeling research, features are often selected purely based on domain knowledge. There lacks a comprehensive methodology to guide the feature selection process when developing building energy forecasting models.In this research, a systematic feature selection procedure for developing building energy forecasting models is proposed in consideration of statistical data analysis, building physics and engineering practices. The procedure includes three main steps: (Step 1) rule-based feature pre-selection process based on domain knowledge. (Step 2) feature removal process through filter methods to remove irrelevant and redundant variables. And (Step 3) Using wrapper method to obtain the best combinations of features.A case study is presented here using simulated building energy data that are generated from a medium sized commercial building (a DOE reference building). In this study, the energy forecasting model generated by using the proposed systematic feature selection process is compared with other models such as a model that uses conventional inputs, and a model with single feature selection technique. The comparison result shows that, in terms of cross validation error, the model with systematic feature selection process shows much better model performance than other models including that with conventional inputs and that uses only single feature selection technique.