Abstract
For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into a series of relatively stable component sequences. For improving the prediction accuracy further by utilizing the current day solar radiation profile in one-hour-ahead predictions, similar solar radiation profile data were selected for training LSTM neural networks. Simulation results show that the hybrid model achieves better prediction performance than traditional prediction methods, such as the exponentially-weighted moving average (EWMA), weather conditioned moving average (WCMA), and only LSTM models.
Highlights
The energy harvesting technique is a promising approach for widening applications of wireless sensor networks (WSNs) in the Internet of Things (IoT) fields by breaking the power limitations and extending the lifetime of the whole network
A multivariate linear regression (MLR) analysis models are based on statistical information, such as standard deviation, variance, mean, and moving model was proposed to generate solar energy prediction with probabilities [17]
There are machine learning-based techniques, such as neural networks (NN) [8] and fuzzy logic (FL) [9], to build quite a lot of prediction methods on time series in general, we focus more heavily on typical models to handle time series
Summary
The energy harvesting technique is a promising approach for widening applications of wireless sensor networks (WSNs) in the Internet of Things (IoT) fields by breaking the power limitations and extending the lifetime of the whole network. The efficiency of solar energy is affected by factors such as geographical location, sun illumination time, and lighting trend. Accurate energy prediction methods for each node have significant importance in EH-WSNs [2]. Time series prediction methods play a very important role in these practical engineering fields, such as energy and information technology [3].
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