Kosunalp S.20.04.20192019-04-2020.04.20192019-04-2020162169-3536https://dx.doi.org/10.1109/ACCESS.2016.2606541https://hdl.handle.net/20.500.12403/654Traditional wireless sensor networks (WSNs) face the problem of a limited-energy source, typically batteries, resulting in the need for careful and effective utilization of the energy source. However, inevitable energy depletion will eventually disturb the operation of a WSN. Energy harvesting (EH) technology is acquiring particular interest, because it has the potential to provide a continuous energy supply in battery-powered WSNs. Solar energy is the most effective environmental energy for EH-WSNs because of its high energy intensity, which comes from a non-controllable source. Therefore, the prediction of future energy availability is a critical issue, as the amount of the harvestable energy may vary over time. In this paper, a novel solar energy prediction algorithm with Q-learning, called Q-learning-based solar energy prediction (QL-SEP), is proposed. Q-learning is an effective way of predicting future actions based on past observations. The distinctive feature of QL-SEP is that not only past days' observations but also the current weather conditions are considered for prediction. The performance of QL-SEP is simulated in this paper using real-world measurements obtained from a solar panel in comparison with the state-of-art approaches. © 2016 IEEE.eninfo:eu-repo/semantics/openAccessEnergy harvestingprediction algorithmssolar energywireless sensor networksElectric batteriesEnergy harvestingForecastingLearning algorithmsSolar energyContinuous energyEnergy depletionEnergy intensityEnergy predictionEnvironmental energyLimited energiesPrediction algorithmsWireless sensor network (WSNs)Wireless sensor networksEnergy harvestingprediction algorithmssolar energywireless sensor networksElectric batteriesEnergy harvestingForecastingLearning algorithmsSolar energyContinuous energyEnergy depletionEnergy intensityEnergy predictionEnvironmental energyLimited energiesPrediction algorithmsWireless sensor network (WSNs)Wireless sensor networksArticle45755576310.1109/ACCESS.2016.26065412-s2.0-85027046574Q1WOS:000386078300017Q1