LI Xiangyu, XIE Nijie. Multi-algorithm Fusion Framework for Energy Prediction of Energy Harvesting IoT Node and Implementation[J]. Chinese Journal of Electronics, 2020, 29(5): 952-958. doi: 10.1049/cje.2020.08.011
Citation: LI Xiangyu, XIE Nijie. Multi-algorithm Fusion Framework for Energy Prediction of Energy Harvesting IoT Node and Implementation[J]. Chinese Journal of Electronics, 2020, 29(5): 952-958. doi: 10.1049/cje.2020.08.011

Multi-algorithm Fusion Framework for Energy Prediction of Energy Harvesting IoT Node and Implementation

doi: 10.1049/cje.2020.08.011
Funds:  This work is supported by the Research and Development Plan in Key Areas of Guangdong Province, China (No.2019B010117002).
  • Received Date: 2019-12-18
  • Rev Recd Date: 2020-05-18
  • Publish Date: 2020-09-10
  • Accurate harvested energy prediction of the energy harvesting Internet-of-things (IoT) nodes is the basis of the proper power management and should be lowoverhead. A new multi-algorithm fusion framework, which merges results of multiple prediction algorithms to achieve a higher accuracy, has been proposed. A three-algorithm fusion solar radiation predictor was implemented. The experiments using the real solar radiation data show that it improves the percentage prediction error by 10%-26% for different prediction intervals. Its complexity is low enough to run on the embedded systems in real-time.
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  • R. J. M. Vullers, R. van Schaijk, I. Doms, et al., "Micropower energy harvesting", Solid-State Electronics, Vol.53, No.7, pp.684-693, 2009.
    N. Vai, S. Gong, H. Shi, et al., "Improving throughput of communication link in IEEE 802.15.4 based energy-harvesting wireless sensor network", Chinese Journal of Electronics, Vol.28, No.4, pp.841-849, 2019.
    F. Yan, J. Zhao, H. Qu, et al., "Energy-efficient cooperative strategy in RF energy harvesting cognitive radio network", Chinese Journal of Electronics, Vol.28, No.3, pp.651-657, 2019.
    A. Kansal, J. Hsu, S. Zahedi, et al., "Power management in energy harvesting sensor networks", ACM Transactions on Embedded Computing Systems, Vol.6, No.4, Page 32, 2007.
    X. Li, N. Xie and X. Tian, "Dynamic voltage-frequency and workload joint scaling power management for energy harvesting multi-core WSN node SoC", Sensors, Vol.17, No.2, DOI:10.3390/s17020310, 2017.
    E. Ibarra, A. Antonopoulos, E. Kartsakli, et al., "QoS-aware energy management in body sensor nodes powered by human energy harvesting", IEEE Sensors Journal, Vol.16, No.2, pp.542-549, 2016.
    L. Liu, Y. Zhao, D. Chang, et al., "Prediction of short-term PV power output and uncertainty analysis", Applied Energy, Vol.228, No.15, pp.700-711, 2018.
    J. R. PiornoCarlo, C. Bergonzini, D. Atienza, et al., "Prediction and management in energy harvested wireless sensor nodes", Proc. of 1st Int. Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology Conf., Aalborg, Denmark, pp.6-10, 2009.
    M. Hassan and A. Bermak, "Solar Harvested energy prediction algorithm for wireless sensors", Proc. of 4th Asia Symposium on Quality Electronic Design, Penang, Malaysia, pp.178-181, 2012.
    R. Huang, T. Huang, R. Gadh, et al., "Solar generation prediction using the ARMA model in a laboratory-level microgrid", Proc. of 3rd Int. Conf. on Smart Grid Communications, Tainan, China, pp.528-533, 2012.
    C. Bergonzini, D. Brunelli and L. Benini, "Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters", Microelectronics Journal, Vol.41, No.11, pp.766-777, 2010.
    J. Wu, C. K. Chang, Y. Zhang, et al., "Prediction of solar radiation with genetic approach combing multi-model framework", Renewable Energy, Vol.66, pp.132-139, 2014.
    Y. Bao, X. Wang, X. Liu, et al., "Solar radiation prediction and energy allocation for energy harvesting base stations", Proc. of IEEE Int. Conf. on Communications, Sydney, NSW, Australia, pp.3487-3492, 2014.
    Q. Liu and Q. Zhang, "Accuracy improvement of energy prediction for solar-energy-powered embedded systems", IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol.24, No.6, pp.2062-2074, 2016.
    D. L. Hall and J. Llinas, "An introduction to multisensor data fusion", Proceedings of the IEEE, Vol.85, No.1, pp.6-23, 1997.
    M. Azmi, S. Araghinejad and M. Kholghi, "Multi model data fusion for hydrological forecasting using K-nearest neighbour method", Iranian Journal of Science and Technology, Vol.34, No.B1 pp.81-92, 2010.
    S. Gao, Y. Zhong and W. Li, "Random weighting method for multisensor data fusion", IEEE Sensors Journal, Vol.11, No.9, pp.1955-1961, 2011.
    L. Ma, B. Li, Z. B. Yang, et al., "A new combination prediction model for short-term wind farm output power based on meteorological data collected by WSN", International Journal of Control and Automation, Vol.7, No.1, pp.171-180, 2014.
    J. Ramos and A. Andreas. "University of Texas Panamerican (UTPA), Solar Radiation Lab (SRL), Edinburg, Texas (Data)", NREL Report, No.DA-5500-56514, 2011.
    A. Andreas and S. Wilcox. "Observed atmospheric and solar information system (OASIS), Tucson, Arizona (Data)", NREL Report, No.DA-5500-56494, 2010.
    K. Olson and A. Andreas, "Natural energy laboratory of Hawaii authority (NELHA):Hawaii ocean science & technology Park, Kailua-Kona, Hawaii (Data)", NREL Report, No.DA-5500-64450, 2012.
    K. C. Divya and O/. Jacob, "Battery energy storage technology for power systems-An overview", Electric Power Systems Research, Vol.79, No.4, pp.511-520, 2009.
    N. Poole, Y. Mo, J. Brusey, et al., "Power consumption of microelectronic equipment for wireless sensor networks," Nanotech. NSTI, Vol.1, pp.534-537, 2009.
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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