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Analysis on intelligent machine learning enabled with meta-heuristic algorithms for solar irradiance prediction

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Abstract

The solar forecasting is an effective method to enhance the operation of an electrical system for merging a large amount of solar power generation systems and intends to expand a new empirical method to model the prediction uncertainty of the solar irradiance. The proposed model comprises three phases, such as (a) Data Acquisition, (b) Feature Extraction, and (c) Prediction. Initially, benchmark data available from local meteorological organizations are collected that includes the numerical weather forecasting data like temperature, dew point, humidity, visibility, wind speed, and other descriptive information. Once the data is collected, feature extraction is done by first-order and second-order statistical models. First Order Statistics, like mean, median, standard deviation, the maximum value of entire data, and minimum value of entire data, and Second-Order Statistics, like Kurtosis, skewness, correlation, and entropy are extracted as the features. These features are further applied to three machine learning algorithms named Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). As main novelty of this paper, the number of hidden neurons of all these networks is optimized by a hybrid algorithm merging both the Deer Hunting Optimization Algorithm (DHOA) and Grey Wolf Optimization (GWO), which is named as Grey Updated DHOA (GU-DHOA). The improvement of these networks with the assistance of a hybrid meta-heuristic algorithm will be highly effective for solar irradiance prediction, overcoming the existing machine learning algorithms.

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Abbreviations

BIPV:

Building Integrated Photo Voltaics

STC:

Solar Thermal Collectors

GWO:

Grey Wolf Optimization

DHOA:

Deer Hunting Optimization Algorithm

PV:

Photo Voltaics

GHI:

Global Horizontal Irradiance

DNI:

Direct Normal Irradiance

ARIMA:

Autoregressive Integrated Moving Average

ARMAX:

Autoregressive Moving Average with Exogenous Inputs

ARMA:

Autoregressive Moving Average

SVM:

Support Vector Machine

ANN:

Artificial Neural Networks

LSTM:

Long Short-Term Memory

BPNN:

Back Propagation Neural Network

RMSE:

Root Mean Square Error

PDF:

Probability Distribution Function

NWP:

Numerical Weather Prediction

ESS:

Energy Storage System

rRMSE:

relative RMSE

MLP:

MultiLayer Perceptron

GU-DHOA:

Grey Updated DHOA

CNN:

Convolutional Neural Network

MEP:

Mean Error Percentage

RNN:

Recurrent Neural Network

DNN:

Deep Neural Network

MASE:

Mean Absolute Scaled Error

SMAPE:

Symmetric Mean Absolute Percentage Error

GRU:

Gate Recurrent Unit

MSE:

Mean Square Error

MAE:

Mean Absolute Error

NN:

Neural Networks

GA/PSO:

Genetic Algorithm/Particle Swarm Optimization

RBF:

Radical Basis Function

GBRT:

Gradient Boosted Regression Trees

MJ:

Mega Joule

WCO:

World Cup Optimization

IDHOA:

Improved Deer Hunting Optimization Algorithm

ANNs:

Artificial Neural Networks

GSAPSO:

Gravitational Search Algorithm and Particle Swarm Optimization

Q-IWO:

Quantum-Invasive Weed Optimization

SFO:

Sunflower Optimization Algorithm

MR-ESN:

Multi-Reservoir Echo State Network

References

  1. Bae KY, Jang HS, Sung DK (2017) Hourly solar irradiance prediction based on support vector machine and its error analysis. IEEE Trans Power Syst 32(2):935–945

    Google Scholar 

  2. Basurto N, Arroyo A, Vega R, Quintián H, Calvo-Rolle JL, Herrero A (2019) A hybrid intelligent system to forecast solar energy production. Comput Electr Eng 78:373–387

    Article  Google Scholar 

  3. Heo Y, Liao W, Xu S (2019) Simplified vector-based model tailored for urban-scale prediction of solar irradiance. Sol Energy 183:566–586

    Article  Google Scholar 

  4. Gueymard CA, Habte A, Sengupta M (2018) Reducing uncertainties in large-scale solar resource data: the impact of aerosols. IEEE J Photovolt 8(6):1732–1737

    Article  Google Scholar 

  5. Plonski PA, Hook JV, Isler V (2016) Environment and solar map construction for solar-powered mobile systems. IEEE Trans Rob 32(1):70–82

    Article  Google Scholar 

  6. Alsadi SY, Nassar YF (2017) Estimation of solar irradiance on solar fields: an analytical approach and experimental results. IEEE Trans Sustain Energy 8(4):1601–1608

    Article  Google Scholar 

  7. Sauer KJ, Roessler T, Hansen CW (2015) Modeling the irradiance and temperature dependence of photovoltaic modules in PVsyst. IEEE J Photovolt 5(1):152–158

    Article  Google Scholar 

  8. Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468

    Article  Google Scholar 

  9. Lorenz E, Hurka J, Heinemann D, Beyer HG (2009) Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J Sel Top Appl Earth Obs Remote Sens 2(1):2–10

    Article  Google Scholar 

  10. Dong N, Chang J-F, Wu A-G, Gao Z-K (2020) A novel convolutional neural network framework based solar irradiance prediction method. Int J Electr Power Energy Syst 114:105411

    Article  Google Scholar 

  11. Gustafson WT, Bender G, Leahy LV (2016) Global validation of REST2 incorporated into an operational DNI and GHI irradiance model. In: IEEE 43rd photovoltaic specialists conference (PVSC), Portland, OR, 2016, pp 0947–0952

  12. Lago J, De Brabandere K, De Ridder F, De Schutter B (2018) Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data. Sol Energy 173:566–577

    Article  Google Scholar 

  13. Gostein M, Stueve B, Passow K, Panchula A (2016) Evaluating a model to estimate GHI, DNI, and DHI from POA irradiance. In: IEEE 43rd photovoltaic specialists conference (PVSC), Portland, OR, 2016, pp 0943–0946

  14. Chai S, Xu Z, Wong WK (2016) Optimal granule-based pis construction for solar irradiance forecast. IEEE Trans Power Syst 31(4):3332–3333

    Article  Google Scholar 

  15. Weng Y, Wang X, Hua J, Wang H, Kang M, Wang F (2019) Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. IEEE Trans Comput Soc Syst 6(3):547–553

    Article  Google Scholar 

  16. Liu H et al (2017) ARMAX-based transfer function model identification using wide-area measurement for adaptive and coordinated damping control. IEEE Trans Smart Grid 8(3):1105–1115

    Article  Google Scholar 

  17. Li J et al (2019) A stable autoregressive moving average hysteresis model in flexure fast tool servo control. IEEE Trans Autom Sci Eng 16(3):1484–1493

    Article  Google Scholar 

  18. Pelaez SA, Deline C, MacAlpine SM, Marion B, Stein JS, Kostuk RK (2019) Comparison of bifacial solar irradiance model predictions with field validation. IEEE J Photovolt 9(1):82–88

    Article  Google Scholar 

  19. Henney CJ, Hock RA, Schooley AK, Toussaint WA, White SM, Arge CN (2015) Forecasting solar extreme and far ultraviolet irradiance. Space Weather 13(3):141–153

    Article  Google Scholar 

  20. Li X, Jia X, Wang L, Zhao K (2015) On spectral unmixing resolution using extended support vector machines. IEEE Trans Geosci Remote Sens 53(9):4985–4996

    Article  Google Scholar 

  21. Tanaka T, Kawakami W, Kuwabara S, Kobayashi S, Hirano A (2019) Intelligent monitoring of optical fiber bend using artificial neural networks trained with constellation data. IEEE Netw Lett 1(2):60–62

    Article  Google Scholar 

  22. Gibson K, Cole IR, Goss B, Betts TR, Gottschalg R (2017) Compensation of temporal averaging bias in solar irradiance data. IET Renew Power Gener 11(10):1288–1294

    Article  Google Scholar 

  23. Fatemi SA, Kuh A, Fripp M (2018) Parametric methods for probabilistic forecasting of solar irradiance. Renew Energy 129(part A):666–676

    Article  Google Scholar 

  24. Kakimoto M, Endoh Y, Shin H, Ikeda R, Kusaka H (2019) Probabilistic solar irradiance forecasting by conditioning joint probability method and its application to electric power trading. IEEE Trans Sustain Energy 10(2):983–993

    Article  Google Scholar 

  25. Paulescu M, Paulescu E (2019) Short-term forecasting of solar irradiance. Renew Energy 143:985–994

    Article  Google Scholar 

  26. Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic optimization algorithm inspired by FIFA World Cup competitions: theory and its application in PID designing for AVR system. J Control Autom Electr Syst 27(4):419–440

    Article  Google Scholar 

  27. Razmjooy N, Ramezani M (2016) Training wavelet neural networks using hybrid particle swarm optimization and gravitational search algorithm for system identification. Int J Mechatron Electr Comput Technol 6(21):2987–2997

    Google Scholar 

  28. Razmjooy N, Ramezani M (2014) An improved quantum evolutionary algorithm based on invasive weed optimization. Indian J Sci Res 4(2):413–422

    Google Scholar 

  29. Yuan Z, Wang W, Wang H, Razmjooy N (2020) A new technique for optimal estimation of the circuit-based PEMFCs using developed sunflower optimization algorithm. Energy Rep 6:662–671

    Article  Google Scholar 

  30. Tian M-W, Yan S-R, Han S-Z, Nojavan S, Jermsittiparsert K, Razmjooy N (2020) New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. J Clean Prod 249:119414

    Article  Google Scholar 

  31. Li Q, Zhou W, Ling R, Feng L, Liu K (2020) Multi-reservoir echo state computing for solar irradiance prediction: a fast yet efficient deep learning approach. Appl Soft Comput 95:106481

    Article  Google Scholar 

  32. Kamadinata JO, Ken TL, Suwa T (2019) Sky image-based solar irradiance prediction methodologies using artificial neural networks. Renew Energy 134:837–845

    Article  Google Scholar 

  33. Fernández-Navarro F, Carbonero-Ruz M, Becerra Alonso D, Torres-Jiménez M (2017) Global sensitivity estimates for neural network classifiers. IEEE Trans Neural Netw Learn Syst 28(11):2592–2604

    Article  MathSciNet  Google Scholar 

  34. Namatevs I (2017) Deep convolutional neural networks: structure, feature extraction and training. Inf Technol Manag Sci 20:40–47

    Google Scholar 

  35. Li F, Liu M, Alzheimer’s Disease Neuroimaging Initiative (2019) A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer’s disease. J Neurosci Methods 323:108–118

  36. Brammya G, Praveena S, Ninu Preetha NS, Ramya R, Rajakumar BR, Binu D (2019) Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput J. https://doi.org/10.1093/comjnl/bxy133

  37. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  38. Pedersen MEH, Chipperfield AJ (2010) Simplifying particle swarm optimization. Appl Soft Comput 10(2):618–628

    Article  Google Scholar 

  39. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore, India

    T. Vaisakh & R. Jayabarathi

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  1. T. Vaisakh
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  2. R. Jayabarathi
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Vaisakh, T., Jayabarathi, R. Analysis on intelligent machine learning enabled with meta-heuristic algorithms for solar irradiance prediction. Evol. Intel. 15, 235–254 (2022). https://doi.org/10.1007/s12065-020-00505-6

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  • DOI: https://doi.org/10.1007/s12065-020-00505-6

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