Kosunalp S.20.04.20192019-04-2020.04.20192019-04-2020170360-5442https://dx.doi.org/10.1016/j.energy.2017.05.175https://hdl.handle.net/20.500.12403/457Energy 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. © 2017 Elsevier Ltdeninfo:eu-repo/semantics/closedAccessEnergy harvestingEnergy predictionWind powerWireless sensor networksEnergy harvestingForecastingWind powerDiurnal cycleEnergy generationsEnergy predictionEnvironmental energyFuture energiesReal measurementsTime slotsWireless sensor network (WSNs)Wireless sensor networksalgorithmenergy efficiencyenergy resourcenetwork analysispower generationpredictionsensorwind powerEnergy harvestingEnergy predictionWind powerWireless sensor networksEnergy harvestingForecastingWind powerDiurnal cycleEnergy generationsEnergy predictionEnvironmental energyFuture energiesReal measurementsTime slotsWireless sensor network (WSNs)Wireless sensor networksalgorithmenergy efficiencyenergy resourcenetwork analysispower generationpredictionsensorwind powerArticle1391275128010.1016/j.energy.2017.05.1752-s2.0-85020443396Q1WOS:000414879500098Q1