An Online Machine Learning Algorithm for Heat Load Forecasting in District Heating Systems
作者： Spyridon Provatas
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摘要: Context. Heat load forecasting is an important part of district heating optimization. In particular, energy companies aim at minimizing peak boiler usage, optimizing combined heat and power generat ...
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Ezilda Almeida, Carlos Ferreira, João Gama, Adaptive Model Rules from Data Streams european conference on machine learning. pp. 480- 492 ,(2013) , 10.1007/978-3-642-40988-2_31
Fredrik Wernstedt, Paul Davidsson, Christian Johansson, Deployment of Agent Based Load Control in District Heating Systems adaptive agents and multi agents systems. ,(2010)
Fredrik Wernstedt, Paul Davidsson, Christian Johansson, Smart Heat Grid on an Intraday Power Market 3rd International Workshop on Agent Technologies for Energy Systems, Valencia, Spain (2012). ,(2012)
Christophe Giraud-Carrier, A note on the utility of incremental learning Ai Communications. ,vol. 13, pp. 215- 223 ,(2000)
Nikunj C Oza, Stuart J Russell, Online bagging and boosting systems, man and cybernetics. ,vol. 3, pp. 2340- 2345 ,(2005) , 10.1109/ICSMC.2005.1571498
Sanjay Jain, Steffen Lange, Sandra Zilles, Towards a better understanding of incremental learning algorithmic learning theory. pp. 169- 183 ,(2006) , 10.1007/11894841_16
Vlado Stankovski, Jernej Trnkoczy, Application of Decision Trees to Smart Homes Designing Smart Homes. pp. 132- 145 ,(2006) , 10.1007/11788485_8
Christian Johansson, Fredrik Wernstedt, Paul Davidsson, A case study on availability of sensor data in agent cooperation intelligent distributed computing. ,vol. 7, pp. 111- 120 ,(2010) , 10.2298/CSIS100118011J
Sven Werner, District Heating and Cooling ,(2013)
Thomas G. Dietterich, An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization Machine Learning. ,vol. 40, pp. 139- 157 ,(2000) , 10.1023/A:1007607513941