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您的检索式：作者名="Chengchu Yan"
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| 1 | Development of a key-variable-based parallel HVAC energy predictive model显示文摘Building heating,ventilation,and air conditioning(HVAC)systems consume large amounts of energy,and precise energy prediction is necessary for developing various energy-efficiency strategies.Energy prediction using data-driven models has received increasing attention in recent years.Typically,two types of driven models are used for building energy prediction:sequential and parallel predictive models.The latter uses the historical energy of the target building as training data to predict future energy consumption.However,for newly built buildings or buildings without historical data records,the energy can be estimated using the parallel model,which employs the energy data of similar buildings as training data.The second predictive model is seldom studied because the model input feature is difficult to identify and collect.Herein,we propose a novel key-variable-based parallel HVAC energy predictive model.This model has informative input features(including meteorological data,occupancy activity,and key variables representing building and system characteristics)and a simple architecture.A general key-variable screening toolkit which was more versatile and flexible than present parametric analysis tools was developed to facilitate the selection of key variables for the parallel HVAC energy predictive model.A case study is conducted to screen the key variables of hotel buildings in eastern China,based on which a parallel chiller energy predictive model is trained and tested.The average cross-test error measured in terms of the coefficient of variation of the root mean square error(CV-RMSE)and normalized mean bias error(NMBE)of the parallel chiller energy predictive model is approximately 16%and 8.3%,which is acceptable for energy prediction without using historical energy data of the target building. | Huajing Sha Peng Xu Chengchu Yan Ying Ji Kenan Zhou Feiran Chen | 2022 | Building Simulation2022，15，7： | 1 |
| 2 | Quantitative energy performance assessment methods for existing buildings 显示文摘 | Wang Shengwei Yan Chengchu Xiao Fu | 2012 | Energy and Buildings2012，，55： | 1 |
| 3 | An improved transfer learning strategy for short-term cross-building energy prediction usingdata incremental显示文摘The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%. | Guannan Li Yubei Wu Chengchu Yan Xi Fang Tao Li Jiajia Gao Chengliang Xu Zixi Wang | 2024 | Building Simulation2024，17，1： | 0 |
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