Short-Term 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 low-cost 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) | |
Online Adaptive mode | |
Online Prediction |
Prediction Approach | MAE | MSE | RMSE |
---|---|---|---|
ARMA | 0.3124 | 0.2133 | 0.4463 |
FFNN-PSO | 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. Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data. Sensors 2022, 22, 8218. https://doi.org/10.3390/s22218218
Alzahrani A. Short-Term 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. "Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data" Sensors 22, no. 21: 8218. https://doi.org/10.3390/s22218218