A New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks With Q-Learning
Küçük Resim Yok
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Traditional 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.
Açıklama
Anahtar Kelimeler
Energy harvesting, prediction algorithms, solar energy, wireless sensor networks, Electric batteries, Energy harvesting, Forecasting, Learning algorithms, Solar energy, Continuous energy, Energy depletion, Energy intensity, Energy prediction, Environmental energy, Limited energies, Prediction algorithms, Wireless sensor network (WSNs), Wireless sensor networks, Energy harvesting, prediction algorithms, solar energy, wireless sensor networks, Electric batteries, Energy harvesting, Forecasting, Learning algorithms, Solar energy, Continuous energy, Energy depletion, Energy intensity, Energy prediction, Environmental energy, Limited energies, Prediction algorithms, Wireless sensor network (WSNs), Wireless sensor networks
Kaynak
IEEE Access
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
4
