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An energy prediction algorithm for wind-powered wireless sensor networks with energy harvesting

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  • Kosunalp, Selahattin

Abstract

Energy harvesting (EH) from environmental energy sources has the potential to ensure unlimited, uncontrollable and unreliable energy for wireless sensor networks (WSNs), bringing a need to predict future energy availability for the effective utilization of the harvested energy. The majority of previous prediction approaches have exploited the diurnal cycle dividing the whole day into equal-length time slots in which predictions were carried out in each slot independently. This is not, however, efficient for wind energy as it exhibits non-controllable behaviour in that the amount of energy to be harvested varies over time. This paper proposes a novel approach to predict the wind energy for EH-WSNs depending on the energy generation profile of latest condition. The distinctive feature of the proposed approach is to consider the recent conditions in current-day, instead of past-day’s energy generation profiles. The performance of the proposed algorithm is evaluated using real measurements in comparison with state-of-art approaches. Results show that the proposed strategy significantly outperforms the two popular energy predictors, EWMA and Pro-Energy.

Suggested Citation

  • Kosunalp, Selahattin, 2017. "An energy prediction algorithm for wind-powered wireless sensor networks with energy harvesting," Energy, Elsevier, vol. 139(C), pages 1275-1280.
    • Handle: RePEc:eee:energy:v:139:y:2017:i:c:p:1275-1280
    DOI: 10.1016/j.energy.2017.05.175
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    References listed on IDEAS

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    1. Yang, Feng & Du, Lin & Chen, Weigen & Li, Jian & Wang, Youyuan & Wang, Disheng, 2017. "Hybrid energy harvesting for condition monitoring sensors in power grids," Energy, Elsevier, vol. 118(C), pages 435-445.
    2. Gherbi, Chirihane & Aliouat, Zibouda & Benmohammed, Mohamed, 2016. "An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks," Energy, Elsevier, vol. 114(C), pages 647-662.
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    Cited by:

    1. Meihua Wang & Wei-Chang Yeh & Ta-Chung Chu & Xianyong Zhang & Chia-Ling Huang & Jun Yang, 2018. "Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm," Energies, MDPI, vol. 11(9), pages 1-23, September.
    2. Wang, Junlei & Tang, Lihua & Zhao, Liya & Zhang, Zhien, 2019. "Efficiency investigation on energy harvesting from airflows in HVAC system based on galloping of isosceles triangle sectioned bluff bodies," Energy, Elsevier, vol. 172(C), pages 1066-1078.

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