Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms
暂无分享,去创建一个
Jing Liu | Xin Chen | Zhigang Zhou | Yi Jiang | Puning Xue | Xiumu Fang | Yi Jiang | Xin Chen | Puning Xue | Jing Liu | Zhigang Zhou | Xiumu Fang
[1] Erik Dotzauer,et al. Simple model for prediction of loads in district-heating systems , 2002 .
[2] C. Goose,et al. Glossary of Terms , 2004, Machine Learning.
[3] T. Agami Reddy,et al. Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051) , 2007 .
[4] Masatoshi Sakawa,et al. Heat load prediction through recurrent neural network in district heating and cooling systems , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.
[5] K. Wojdyga,et al. An influence of weather conditions on heat demand in district heating systems , 2008 .
[6] Diyar Akay,et al. Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting , 2009, Expert Syst. Appl..
[7] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[8] Brian Vad Mathiesen,et al. The role of district heating in future renewable energy systems , 2010 .
[9] M. Kozek,et al. Online Short-Term Forecast of System Heat Load in District Heating Networks , 2011 .
[10] U. Persson,et al. Heat distribution and the future competitiveness of district heating , 2011 .
[11] Marc A. Rosen,et al. District heating and cooling: Review of technology and potential enhancements , 2012 .
[12] Amir F. Atiya,et al. A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition , 2011, Expert Syst. Appl..
[13] Nurettin Yamankaradeniz,et al. Exergoeconomic analysis of a district heating system for geothermal energy using specific exergy cost method , 2013 .
[14] Sven Werner. District Heating and Cooling , 2013 .
[15] Jianing Zhao,et al. Energetic and exergetic efficiencies of coal-fired CHP (combined heat and power) plants used in district heating systems of China , 2013 .
[16] Meiping Wang,et al. Application of wavelet neural network on thermal load forecasting , 2013, Int. J. Wirel. Mob. Comput..
[17] Sven Werner,et al. Achieving low return temperatures from district heating substations , 2014 .
[18] Brian Vad Mathiesen,et al. 4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems , 2014 .
[19] Shahaboddin Shamshirband,et al. Evaluation of the most influential parameters of heat load in district heating systems , 2015 .
[20] Shahaboddin Shamshirband,et al. Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm , 2015 .
[21] Fu Xiao,et al. A framework for knowledge discovery in massive building automation data and its application in building diagnostics , 2015 .
[22] Dragan Mitić,et al. Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems , 2015 .
[23] Shahaboddin Shamshirband,et al. Intelligent forecasting of residential heating demand for the District Heating System based on the monthly overall natural gas consumption , 2015 .
[24] Robert Kabacoff,et al. R in Action: Data Analysis and Graphics with R , 2015 .
[25] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[26] Abdul Hanan Abdullah,et al. Heat load prediction in district heating systems with adaptive neuro-fuzzy method , 2015 .
[27] Radiša Jovanović,et al. Ensemble of various neural networks for prediction of heating energy consumption , 2015 .
[28] Brian Vad Mathiesen,et al. Future district heating systems and technologies: On the role of smart energy systems and 4th generation district heating , 2016, Energy.
[29] Shahaboddin Shamshirband,et al. Extreme learning machine for prediction of heat load in district heating systems , 2016 .
[30] Risto Lahdelma,et al. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system , 2016 .
[31] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[32] Shahaboddin Shamshirband,et al. Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm , 2016 .
[33] Saguna Saguna,et al. Applied machine learning: Forecasting heat load in district heating system , 2016 .
[34] Magnus Dahl,et al. Using ensemble weather predictions in district heating operation and load forecasting , 2017 .
[35] Davy Geysen,et al. Operational thermal load forecasting in district heating networks using machine learning and expert advice , 2017, ArXiv.
[36] Fredrik Wallin,et al. Analysis of Key Factors in Heat Demand Prediction with Neural Networks , 2017 .
[37] Sven Werner,et al. International review of district heating and cooling , 2017 .
[38] Davy Geysen,et al. Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms , 2017 .
[39] Fredrik Wallin,et al. An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks , 2017 .
[40] Morgane Colombert,et al. Recov’Heat: An estimation tool of urban waste heat recovery potential in sustainable cities , 2017 .
[41] Jing Liu,et al. Fault detection and operation optimization in district heating substations based on data mining techniques , 2017 .
[42] Bengt Sundén,et al. Medium-term heat load prediction for an existing residential building based on a wireless on-off control system , 2018, Energy.
[43] José A. Candanedo,et al. Forecasting District Heating Demand using Machine Learning Algorithms , 2018, Energy Procedia.
[44] Alain Pascal Goumba,et al. Air source heat pump for domestic hot water supply: Performance comparison between individual and building scale installations , 2018, Energy.
[45] Amanda D. Smith,et al. Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms , 2018, Applied Energy.
[46] Davy Geysen,et al. Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods , 2018, Energy.
[47] M. Hendel,et al. Urban water networks as an alternative source for district heating and emergency heat-wave cooling , 2018, 1804.10041.
[48] Yang Zhao,et al. Deep learning-based feature engineering methods for improved building energy prediction , 2019, Applied Energy.
[49] Alain Pascal Goumba,et al. Comparison of Direct and Indirect Active Thermal Energy Storage Strategies for Large-Scale Solar Heating Systems , 2019, Energies.
[50] Xiaofeng Guo,et al. Modeling and forecasting building energy consumption: A review of data-driven techniques , 2019, Sustainable Cities and Society.