研究生学术报告预告登记(开题、中期、答辩)

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报告人: 王冉
学号: 1016214013
学院: 环境科学与工程学院
报告类型: 其他学术报告
日期: 25 December 2018
时间: 9:00 AM
地点: 腾讯会议
导师: 吕石磊
题目: Multi-criteria comprehensive study on predictive algorithm of hourly heating energy consumption for residential buildings
内容提要:

The increasing of building energy necessitates reliable energy consumption prediction. Certain research work is necessary to thoroughly illustrate and compare advantages and disadvantages of various models. Therefore, this study investigated comprehensive trade-off between performances of commonly used forecasting models based on multiple performance metrics. Considering the requirements of actual building energy system, the objectives included accuracy, interpretability, robustness, and efficiency. With actual heating energy, prediction models were established by applying extreme gradient boosting (XGBoost), random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and support vector regression (SVR). A comparison revealed the following: 1) RF exhibits optimal average accuracy (under different training datasets), whereas ANN exhibits contrary properties. 2) The robustness of RF is the highest from adaptation to different training datasets with minimum standard deviation of error; XGBoost and ANN exhibit contrary properties. 3) RF, GBDT, and XGBoost are rendered effectively interpretable. 4) At equivalent accuracy level, ANN and SVR require auxiliary algorithms, whereas other models can achieve reasonable accuracy no tuning required. BPNN's calculation time is of an order magnitude higher than those of other models. Overall, XGBoost exhibits the optimal efficiency. This study can provide guidance for effectively selecting prediction models for energy management.

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登记人: 王冉
登记时间: Wednesday, 31 March 2021, 12:05 PM