2019
DOI: 10.3390/en12244762
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A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks

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 … Show more

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Cited by 20 publications
(9 citation statements)
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“…Certain studies employ numerical models like the Weather Research and Forecasting model (WRF) to simulate atmospheric conditions and integrate them with other data sources to generate solar irradiance predictions [19]. Several articles highlight the use of open-access data sources, such as open-source datasets, publicly available Satellite Images, and meteorological data from organizations like NREL and NSRDB [19,70]. It is important to note that the analysis presented is based on the provided information, and the actual techniques used in each study may vary.…”
Section: Data Resourcesmentioning
confidence: 99%
See 1 more Smart Citation
“…Certain studies employ numerical models like the Weather Research and Forecasting model (WRF) to simulate atmospheric conditions and integrate them with other data sources to generate solar irradiance predictions [19]. Several articles highlight the use of open-access data sources, such as open-source datasets, publicly available Satellite Images, and meteorological data from organizations like NREL and NSRDB [19,70]. It is important to note that the analysis presented is based on the provided information, and the actual techniques used in each study may vary.…”
Section: Data Resourcesmentioning
confidence: 99%
“…Recurrent Neural Networks have been proposed for solar radiation nowcasting, enabling short-term forecasting [16]. Cloud Motion Vectors have been incorporated by using the Deep Flow algorithm to enhance the accuracy of solar radiation predictions [70]. Moreover, the European Solar Radiation Atlas (ESRA) clear-sky irradiation model has been utilized in combination with other algorithms to improve solar power nowcasting [19].…”
Section: Prevalence Of Artificial Intelligencementioning
confidence: 99%
“…The agent can adjust its duty cycle according to the node and environmental state. To achieve the maximum overall throughput during a period of operation time (usually a whole day), we also utilize the periodical predicted energy information of each node from the energy predictor proposed in Ge et al 34 The uneven clustering protocol is deployed in our system. For each node S i , the location (x i , y i ) is known.…”
Section: System Modelmentioning
confidence: 99%
“…Therefore, compared to an MLP, LSTM displays better performance when predicting a time series. In particular, long short-term memory (LSTM) networks have been used to predict solar irradiance due to their strong time series-learning ability [23,24].…”
Section: Introductionmentioning
confidence: 99%