A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings

作者: Federico Divina , Miguel García Torres , Francisco A. Goméz Vela , José Luis Vázquez Noguera

DOI: 10.3390/EN12101934

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摘要: … an energy efficiency of at least 20 % [4]. Despite the fact that many industries contribute to such emissions, the building and building construction … Moreover, in the tables, we also report …

参考文章(52)
Fredrik Wallin, Daniel Torstensson, Javier Campillo, Iana Vassileva, Energy Demand Model Design for Forecasting Electricity Consumption and Simulating Demand Response Scenarios in Sweden 4th International Conference in Applied Energy 2012, July 5-8, 2012. Suzhou, China.. ,(2012)
Jakub Nowotarski, Bidong Liu, Rafał Weron, Tao Hong, Improving short term load forecast accuracy via combining sister forecasts Energy. ,vol. 98, pp. 40- 49 ,(2016) , 10.1016/J.ENERGY.2015.12.142
Agoston E. Eiben, J. E. Smith, Introduction to evolutionary computing ,(2003)
Alicia Troncoso Lora, Jesús Manuel Riquelme Santos, José Cristóbal Riquelme, Antonio Gómez Expósito, José Luís Martínez Ramos, Time-series prediction: Application to the short-term electric energy demand Lecture Notes in Computer Science. pp. 577- 586 ,(2004) , 10.1007/978-3-540-25945-9_57
Dandan Liu, Qijun Chen, Kazuyuki Mori, Time series forecasting method of building energy consumption using support vector regression 2015 IEEE International Conference on Information and Automation. pp. 1628- 1632 ,(2015) , 10.1109/ICINFA.2015.7279546
Jerome H. Friedman, Greedy function approximation: A gradient boosting machine. Annals of Statistics. ,vol. 29, pp. 1189- 1232 ,(2001) , 10.1214/AOS/1013203451
Muhammad Qamar Raza, Abbas Khosravi, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings Renewable & Sustainable Energy Reviews. ,vol. 50, pp. 1352- 1372 ,(2015) , 10.1016/J.RSER.2015.04.065
Guo-Feng Fan, Li-Ling Peng, Wei-Chiang Hong, Fan Sun, Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression Neurocomputing. ,vol. 173, pp. 958- 970 ,(2016) , 10.1016/J.NEUCOM.2015.08.051
Max Kuhn, Building Predictive Models in R Using the caret Package Journal of Statistical Software. ,vol. 28, pp. 1- 26 ,(2008) , 10.18637/JSS.V028.I05
Sanjay Kelo, Sanjay Dudul, A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature International Journal of Electrical Power & Energy Systems. ,vol. 43, pp. 1063- 1071 ,(2012) , 10.1016/J.IJEPES.2012.06.009