Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak

Dabbakuti, J. R. K. Kumar; Jacob, Abin; Veeravalli, Venkata Rao; Kumar, Kallakunta Ravi

doi:10.1049/iet-rsn.2019.0394

Cited by 25 publications

(18 citation statements)

References 16 publications

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“…The outcomes of their work have demonstrated dominance over them. the authors in [17] employed a hybrid model in which the TEC time series was divided into its stationary components using VMD. Then a kernel extreme learning machine was used to anticipate the data.…”

Section: Related Workmentioning

confidence: 99%

TEC Forecasting using Optimized Variational Mode Decomposition and Elman Neural Networks

Shakir¹,

Othman²,

Bakar³

2022

IJACSA

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Forecasting the ionosphere layer's total electronic content (TEC) is crucial for its impact on satellite signals and global positioning systems (GPS) and the ability to predict earthquakes. The existing statistical-based forecasting models such as ARMA, ARIMA, and HW suffered from the TEC nonstationarity nature, which requires algorithmic handling of the forecasting and the mathematical part. This study proposes a hybrid method that incorporates several components and is designated as Optimized Variational Mode Decomposition with Recursive Neural Network Forecasting (OVMD-RNN) to forecast TEC. Before using the Elman Network to train each component, Variational Mode Decomposition (VMD) was used to decompose the signal into its essential stationary components. In addition, the proposed method includes an optimization algorithm for determining the best VMD decomposer parameters. The GPS Ionospheric Scintillation and TEC Monitor (GISTM) at Universiti Kebangsaan Malaysia station have been used to evaluate the method based on collected datasets for three years, 2011, 2012, and 2013. The experiment findings show that the model has successfully tracked all the up and down patterns in the time series. The results also reveal that VMD-based training might not always provide good results due to the residual signal. Finally, the evaluation focused on generating loss value and comparing it to the ARIMA benchmark. It showed that OVMD-RNN had accomplished a maximum improvement percentage of ARIMA with a value of (99%).

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Section: Related Workmentioning

confidence: 99%

TEC Forecasting using Optimized Variational Mode Decomposition and Elman Neural Networks

Shakir¹,

Othman²,

Bakar³

2022

IJACSA

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“…To produce the best possible results, the first three VMD components ranging from being used in this study. The aim of this technique is to reduce the nonstationary nature of the time-series data (Dabbakuti et al 2019). The observed tide gauge measurements of each site have been shown on the x-axis while KELM reconstructed tide gauge has been shown the y-axis (Fig.…”

Section: Reconstruction Of Tide Gauge Measurementmentioning

confidence: 99%

Modelling and mitigation of real-time sea level measurement over the coastal area of Japan

Ansari

Bae

2021

Mar Geophys Res

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This study investigates the real-time sea-level measurement from six tide gauge stations in the coastal area of Japan. Initially, the six co-located global navigation satellite station sites vertical velocity has been observed followed by estimation of the relative tide gauge velocities. The results showed that the estimated relative sea-level changes abruptly at different sites depending on the location of tide gauge stations. To accurately analyse the regional tide gauge patterns at each site, a continuous wavelet transforms based on Morse wavelet as a default transformation has been applied. Analysis showed that the peaks of tide gauges have not occurred at a static time at each site, rather they were repeating with the variation of time. The width of the spectrum peaks varied with different sites and the overall spectrum frequency was not unique. These characteristics variation with time may be due to the variables that affect the stability of the sea level measurements. Finally, the kernel extreme learning machine (KELM) approach based on variational mode decomposition is used to reconstruct the tide gauge measurements. The correlation coefficients between observed and reconstructed data sets are very high varying from 0.91 to 0.96. The estimated root mean square error between two data sets is varying from 0.04 to 0.06 m at the entire region. This means that the KELM reconstructed tide gauge showed good agreement with the observed tidal waveforms. Thus, it can be concluded that the proposed techniques can be implemented for effective real-time sea-level monitoring changes with the available data.

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“…IoT-based solutions are proposed by researchers in [56][57][58] to make available appropriate and suitable source management, load shedding, data acquisition, and control of the SAPV systems and monitor and evaluate the electrification projects. [59] show that IoT provides the capability to bring into use the MATLAB, ThingSpeak, and other tools/ functions by granting the authority to one person to operate the forecasting system. Overall, the entire effort lessens.…”

Section: Introductionmentioning

confidence: 99%

A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

Patel

Swathika

Subramaniam

et al. 2022

International Journal of Photoenergy

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Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation.

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Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak

Cited by 25 publications

References 16 publications

TEC Forecasting using Optimized Variational Mode Decomposition and Elman Neural Networks

TEC Forecasting using Optimized Variational Mode Decomposition and Elman Neural Networks

Modelling and mitigation of real-time sea level measurement over the coastal area of Japan

A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

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