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Plus-profile energy harvested prediction and adaptive energy management for solar-powered wireless sensor networks

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Abstract

Wireless sensor networks (WSNs) are mostly used for monitoring the environment; however, they are usually powered by non-rechargeable batteries with limited energy. Solar energy harvesting is an attractive solution to the limit by charging the sensor nodes; however, the harvested solar energy is easily affected by weather conditions. Based on the characteristics of uncertainty and intermittency of solar energy, this paper proposes a plus-profile solar energy prediction algorithm. This algorithm makes the prediction of future available solar energy by finding the data in the dataset that is most similar to the data of the day and combining it with recent weather trend. According to the predicted result, the paper further proposes an adaptive energy management scheme to suit the harvested energy. In the scheme, sensor nodes can adaptively adjust task scheduling to achieve energy neutrality. The simulation results show that compared with other algorithms, the prediction accuracy of the proposed prediction algorithm is improved by 17.7 and 22.4%, respectively, and the proposed energy management scheme reduced energy loss by 6.2 and 46.8%, respectively.

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Availability of data and materials

The datasets are available on the public repositories. See reference 30.

Abbreviations

PP-Energy:

Plus-profile energy harvested prediction

WCMA:

Weather-conditioned moving average

EWMA:

Exponentially weighted moving average

ASEA:

Accurate solar energy allocation

CNN:

Convolutional neural network

LSTM:

Long short-term memory

DDCA:

Dynamic duty cycle adaptation

FQL:

Fuzzy Q-learning

MAPE:

Mean absolute percentage error

NREL:

National Renewable Energy Laboratory

AEM:

Adaptive energy management

RL:

Reinforcement learning

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Funding

This work was supported by the Hubei Provincial Natural Science Foundation of China under Grant No. 2017CKB893 and Wuhan Polytechnic University reform subsidy project Grant No. 03220153.

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  1. School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, 430048, China

    Yuanxiang Wang, Zhen Xu & Lei Yang

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  1. Yuanxiang Wang
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  2. Zhen Xu
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Yuanxiang Wang wrote the original draft, Zhen Xu was involved in project administration, and Lei Yang performed the formal analysis.

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Correspondence to Zhen Xu.

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Wang, Y., Xu, Z. & Yang, L. Plus-profile energy harvested prediction and adaptive energy management for solar-powered wireless sensor networks. J Supercomput 80, 7585–7603 (2024). https://doi.org/10.1007/s11227-023-05755-6

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