Verification of Early Design Stage Machine Learning Model using EnergyPlus Simulation Data

This dataset is used to verify machine learning (ML) energy predictions using the EnergyPlus (EP) simulation. The EP model has been validated for one set of the parameters (SINGH, MANAV MAHAN (2020), “Validation of Early Design Stage EnergyPlus Model for Office Building”, Mendeley Data, V1, doi: 10.17632/x6xwvb2r9r.1). Then, several EP models are developed to generate the data for several combinations of the parameters as required. The pre-trained ML model is used to make prediction for the test dataset. The accuracy of prediction is reported using scatter plot, root mean square error (RMSE) and R2 values. The model shows RMSE of 4.2 MWh/a and R2 value of 0.94. In the scatter, most of the points lie close to the black-dashed line which shows ML predictions are close to the EP simulations. Please note that ML model is developed using a component based approach proposed by Geyer and Singaravel, 2018 and developed further by Singh et al., 2019. Please go through the mentioned paper for more details. P. Geyer, S. Singaravel, Component-based machine learning for performance prediction in building design, Appl. Energy. 228 (2018) 1439–1453. https://doi.org/10.1016/j.apenergy.2018.07.011 M.M. Singh, S. Singaravel, P. Geyer, Improving Prediction Accuracy of Machine Learning Energy Prediction Models, in: B. Kumar, F.P. Rahimian, D. Greenwood, T. Hartmann (Eds.), Proc. 36th CIB W78 2019 Conf., Newcastle, UK, 2019: pp. 102–112本数据集用于基于EnergyPlus（EP）模拟，验证机器学习（Machine Learning，ML）能源预测的效果。该EP模型已针对一组参数完成验证（SINGH, MANAV MAHAN，2020，《办公楼早期设计阶段EnergyPlus模型验证》，Mendeley Data，V1，doi:10.17632/x6xwvb2r9r.1）。随后，按照研究需求针对多组参数组合构建了多个EP模型，以生成对应实验数据集。我们采用预训练的ML模型对测试数据集开展预测。预测精度通过散点图、均方根误差（Root Mean Square Error，RMSE）与决定系数R²进行量化评估与报告。该模型的RMSE为4.2 MWh/a（兆瓦时/年），R²值为0.94。在散点图中，绝大多数数据点均紧邻黑色虚线分布，表明ML预测结果与EP模拟结果高度契合。请注意，本ML模型采用了Geyer与Singaravel于2018年提出、并经Singh等人于2019年进一步完善的基于组件的构建方法。如需了解更多细节，请参阅上述提及的文献。 P. Geyer、S. Singaravel，《建筑设计中基于组件的机器学习性能预测》，《应用能源》，228卷（2018年），第1439–1453页。https://doi.org/10.1016/j.apenergy.2018.07.011 M.M. Singh、S. Singaravel、P. Geyer，《提升机器学习能源预测模型的预测精度》，收录于：B. Kumar、F.P. Rahimian、D. Greenwood、T. Hartmann 编，《第36届CIB W78 2019会议论文集》，英国纽卡斯尔，2019年：第102–112页