Energy consumption prediction methods for embedded systems
作者： Evaldas Zulkas , Edgaras Artemciukas , Dale Dzemydiene , Eleonora Guseinoviene
DOI: 10.1109/EVER.2015.7112932
关键词:
摘要: Human surrounding environment parameters are gathered regularly from electrical signals which converted to digital signal using ADC converters and performing necessary data transformations. The can be estimated as a time series apply standard statistical models. In this study, there analyzed models that help understand find consistent patterns-trends make predictions depending on all previous data. Energy consumption processing prediction methods were presented. Dependency analysis' results when task management with is the special feature of designed measurement system. Transition one state another includes not only estimates current states, but also state.
sci-hub.se 参考文章(11)
Ramazan Bayindir, Erdal Irmak, İlhami Colak, Askin Bektas, Development of a real time energy monitoring platform International Journal of Electrical Power & Energy Systems. ,vol. 33, pp. 137- 146 ,(2011) , 10.1016/J.IJEPES.2010.06.018
H.Brian Hwarng, Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures Omega. ,vol. 29, pp. 273- 289 ,(2001) , 10.1016/S0305-0483(01)00022-6
Shan Gao, Yu He, Hao Chen, Wind speed forecast for wind farms based on ARMA-ARCH model international conference on sustainable power generation and supply. pp. 1- 4 ,(2009) , 10.1109/SUPERGEN.2009.5348142
Mehmet Demirtas, Ibrahim Sefa, Erdal Irmak, Ilhami Colak, Low-cost and high sensitive microcontroller based data acquisition system for renewable energy sources international symposium on power electronics, electrical drives, automation and motion. pp. 196- 199 ,(2008) , 10.1109/SPEEDHAM.2008.4581303
Mohammad Valipour, Mohammad Ebrahim Banihabib, Seyyed Mahmood Reza Behbahani, Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir Journal of Hydrology. ,vol. 476, pp. 433- 441 ,(2013) , 10.1016/J.JHYDROL.2012.11.017
S. Rosiek, F.J. Batlles, A microcontroller-based data-acquisition system for meteorological station monitoring Energy Conversion and Management. ,vol. 49, pp. 3746- 3754 ,(2008) , 10.1016/J.ENCONMAN.2008.05.029
J. V. Fonseca, R. C. L. Oliveira, J. A. P. Abreu, E. Ferreira, M. Machado, Kalman Filter Embedded in FPGA to Improve Tracking Performance in Ballistic Rockets international conference on computer modelling and simulation. pp. 606- 610 ,(2013) , 10.1109/UKSIM.2013.149
H. Ufuk Gökçe, K. Umut Gökçe, Multi dimensional energy monitoring, analysis and optimization system for energy efficient building operations Sustainable Cities and Society. ,vol. 10, pp. 161- 173 ,(2014) , 10.1016/J.SCS.2013.08.004
Hamed Babazadeh, Wenzhong Gao, Lin Cheng, Jin Lin, None, An hour ahead wind speed prediction by Kalman filter 2012 IEEE Power Electronics and Machines in Wind Applications. pp. 1- 6 ,(2012) , 10.1109/PEMWA.2012.6316394
V. Bulbenkiene, A. Pecko, E. Zulkas, A. Kuprinavicius, A. Sokolov, G. Mumgaudis, Energy Sub-Metering Data Acquisition System Elektronika Ir Elektrotechnika. ,vol. 111, pp. 99- 102 ,(2011) , 10.5755/J01.EEE.111.5.366