Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method
Authors:
- Chengliang XuCXChengliang Xu
- Huanxin ChenHCHuanxin Chen
- Jiangyu WangJWJiangyu Wang
- Yabin GuoYabin Guo
- Yue YuanYYYue Yuan
- 1 January 2019
- journal article
- research article
- Published by Elsevier in Building and Environment
- Vol. 148, 128-135
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (51576074)
This publication has 34 references indexed in Scilit:
- Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regressionEnergy Conversion and Management, 2014
- Indoor thermal condition in urban heat Island – Development of a predictive toolBuilding and Environment, 2012
- Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditionsApplied Soft Computing, 2012
- Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, TurkeyIndoor and Built Environment, 2011
- A decision tree method for building energy demand modelingEnergy and Buildings, 2010
- Prediction of indoor temperature and relative humidity using neural network models: model comparisonNeural Computing and Applications, 2008
- Comparison between detailed model simulation and artificial neural network for forecasting building energy consumptionEnergy and Buildings, 2008
- Artificial neural network models for indoor temperature prediction: investigations in two buildingsNeural Computing and Applications, 2006
- Long Short-Term MemoryNeural Computation, 1997
- Econometric Modelling of the Aggregate Time-Series Relationship Between Consumers' Expenditure and Income in the United KingdomThe Economic Journal, 1978