In this paper, we empirically test a new model with the data of US services sector, which is an extension of the 5-factor model in Fama and French (2015) [1]. 3 types of 5 factors (Global, North American and US) are c...In this paper, we empirically test a new model with the data of US services sector, which is an extension of the 5-factor model in Fama and French (2015) [1]. 3 types of 5 factors (Global, North American and US) are compared. Empirical results show the Fama-French 5 factors are still alive! The new model has better in-sample fit than the 5-factor model in Fama and French (2015).展开更多
In buildings,the heating ventilation and air conditioning system(HVAC)creates a comfortable environment for indoor occupants by setting a temperature strategy.However,this approach leads to unreasonable indoor environ...In buildings,the heating ventilation and air conditioning system(HVAC)creates a comfortable environment for indoor occupants by setting a temperature strategy.However,this approach leads to unreasonable indoor environmental comfort and wasted energy because it does not dynamically adjust to changes in environmental and has a long response time.In this study,a high-precision human comfort prediction method for indoor personnel based on time-series analysis is proposed as the control strategy for HVAC systems.The method includes the data pre-processing module,the class imbalance processing module,and the human comfort network model module.We propose the Human-Comfort Bi-directional Long Short-Term Memory(HC-BiLSTM)network to achieve a better human comfort prediction,and the Synthetic Minority Oversampling Technique for Time-series(SMOTE-TS)algorithm to solve the class imbalance problem in human comfort dataset.A public dataset collected in Pennsylvania,USA,was selected for this study to validate the performance of the proposed method.The experimental results show that the human comfort prediction method proposed in this study achieves 0.9482 and 0.9659 on Macro-averaging and Micro-averaging,respectively,which is the highest accuracy in the known related research.展开更多
文摘In this paper, we empirically test a new model with the data of US services sector, which is an extension of the 5-factor model in Fama and French (2015) [1]. 3 types of 5 factors (Global, North American and US) are compared. Empirical results show the Fama-French 5 factors are still alive! The new model has better in-sample fit than the 5-factor model in Fama and French (2015).
基金supported by the National Natural Science Foundation of China(NSFC)Program 62276009.
文摘In buildings,the heating ventilation and air conditioning system(HVAC)creates a comfortable environment for indoor occupants by setting a temperature strategy.However,this approach leads to unreasonable indoor environmental comfort and wasted energy because it does not dynamically adjust to changes in environmental and has a long response time.In this study,a high-precision human comfort prediction method for indoor personnel based on time-series analysis is proposed as the control strategy for HVAC systems.The method includes the data pre-processing module,the class imbalance processing module,and the human comfort network model module.We propose the Human-Comfort Bi-directional Long Short-Term Memory(HC-BiLSTM)network to achieve a better human comfort prediction,and the Synthetic Minority Oversampling Technique for Time-series(SMOTE-TS)algorithm to solve the class imbalance problem in human comfort dataset.A public dataset collected in Pennsylvania,USA,was selected for this study to validate the performance of the proposed method.The experimental results show that the human comfort prediction method proposed in this study achieves 0.9482 and 0.9659 on Macro-averaging and Micro-averaging,respectively,which is the highest accuracy in the known related research.
