ShortTerm Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data
Abstract
:1. Introduction
 It proposes two different approaches to solar irradiance prediction. These two approaches can predict irradiance with high accuracy and relatively less computational time.
 It presents a effective method for online adaptation of the output weight of the ELM method, which has less computational time.
 The developed models are trained, tested, and validated using local data with a 15 min/sample resolution.
 Implementation and testing of the adaptive ELM approach are carried out on a lowcost microcontroller.
2. Site Location and Data Acquirement
3. Theoretical Illustration of Solar Irradiance Prediction Approaches
3.1. Extreme Learning Machine
Random Hidden Nodes for SLFNs
Algorithm 1 ELM algorithm 

3.2. Adaptive Extreme Learning Machine
3.3. Feed Forward Neural Network Based Particle Optimization
4. Prediction Methodology
4.1. Data Preprocessing and Data Cleaning
4.2. Processing Stage
4.3. Post Processing Stage
5. Results and Discussion
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ELM  Extreme learning machine. 
FFNN  Feedforward neural network. 
GHI  Global horizontal irradiance. 
DNI  Direct Normal Irradiance letter acronym. 
PSO  Particle swarm optimization. 
NSRDB  The National Solar Radiation Database. 
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Stage  Equations 

Initial Training (offline)  $\begin{array}{c}{\mathbf{P}}_{\mathbf{OF}}={\left[{\left({\mathbf{C}}_{\mathbf{OF}}^{\top}{\mathbf{W}}_{\mathbf{OF}}{\mathbf{C}}_{\mathbf{OF}}\right)}^{1}\right]}_{\tilde{N}\times \tilde{N}}\\ {\mathbf{\beta}}_{\mathbf{OF}}={\left[{\mathbf{P}}_{\mathbf{OF}}{\mathbf{C}}_{\mathbf{OF}}^{\top}{\mathbf{W}}_{\mathbf{OF}}{\mathbf{Y}}_{\mathbf{OF}}\right]}_{\tilde{N}\times 1}\end{array}$ 
Online Adaptive mode  $\begin{array}{c}\mathbf{A}={\mathbf{P}}_{\mathbf{OF}}{\mathbf{C}}_{\mathbf{ON}}^{\top},\phantom{\rule{0.277778em}{0ex}}\mathbf{B}={\mathbf{C}}_{\mathbf{ON}}\mathbf{A}\\ {\mathbf{\beta}}_{\mathbf{ON}}={\mathbf{\beta}}_{\mathbf{OF}}+\mathbf{A}{\left({\mathbf{W}}_{\mathbf{ON}}^{1}+\mathbf{B}\right)}^{1}\left({\mathbf{Y}}_{\mathbf{ON}}{\mathbf{C}}_{\mathbf{ON}}{\mathbf{\beta}}_{\mathbf{OF}}\right)\end{array}$ 
Online Prediction  $GHI={\mathbf{Y}}_{n+1}=\mathbf{C}(\mathbf{a},{\mathbf{x}}_{n+1}){\mathbf{\beta}}_{\mathbf{ON}}$ 
Prediction Approach  MAE  MSE  RMSE 

ARMA  0.3124  0.2133  0.4463 
FFNNPSO  0.2675  0.1880  0.3684 
Proposed method  0.2444  0.1727  0.3012 
Method  Training Time  Training MSE  Testing Time  Testing MSE 

Initial ELM  0.0252    0.0046  0.2511 
Adaptive ELM  0.2884    0.0062  0.2459 
1 h regression  0.0022  0.3262  0.0007  0.4257 
2 h regression  0.0094  0.4144  0.0005  0.4282 
1 h NN PSO  30.5079  0.2320  0.0912  0.2552 
4 h NN PSO  43.9875  0.1679  0.0020  0.2024 
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Alzahrani, A. ShortTerm Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data. Sensors 2022, 22, 8218. https://doi.org/10.3390/s22218218
Alzahrani A. ShortTerm Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data. Sensors. 2022; 22(21):8218. https://doi.org/10.3390/s22218218
Chicago/Turabian StyleAlzahrani, Ahmad. 2022. "ShortTerm Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data" Sensors 22, no. 21: 8218. https://doi.org/10.3390/s22218218