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Solar Irradiance Prediction Using an Optimized Data Driven Machine Learning Models

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Abstract

For a higher degree of penetration of renewable energy into the controls of the existing power system, an accurate solar energy prediction is necessary. Data-driven algorithms may be used to enhance solar generation forecasts as data has now become readily accessible in large quantities. To address these predicting issues in this research article three machine learning models: Support Vector Regressor (SVR), Multilayer Perceptron (MLP) and Random Forest Regressor (RFR) have been incorporated to forecast the Global Horizontal Irradiance (GHI), Diffused Horizontal Irradiance (DHI), Diffused Normal Irradiance (DNI) based on the spatiotemporal factors. In order to improve the prediction accuracy, the parametric tuning of models has been carried out with the two met heuristic algorithms: Moth Flame Optimization (MFO) and Grey Wolf Optimization (GWO) and also validated with the novel application of Evolve Class Topper Optimization (ECTO) method. Corresponding performance measures, including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Max Error (ME), and Coefficient of Determination (R2), are employed to evaluate each model's performance. The results obtained through a comparative assessment of all machine learning models confirmed that the ECTO based models have outperformed others and the RFR-ECTO model is the best forecasting model having the highest R2 scores of 0.9441, 0.9107 and 0.8882 and the lowest RMSE value of 75.8613 W/m2, 40.8714 W/m2, 94.8916 W/m2 for GHI, DHI and DNI respectively which ensures that the designed predictive model can be implemented for prediction of solar energy.

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Data Availability

On request, the data utilised in this work may be made public.

Abbreviations

IEA :

International Energy Agency

IRENA :

International Renewable Energy Agency

PV :

Photovoltaic

GW :

Gigawatt

LR :

Linear Regression

ANN :

Artificial Neural Network

XGB :

Extreme Gradient Boosting

KNN :

K-Nearest Neighbours

GPR :

Gaussian Process Regression

DNN :

Deep Neural Network

PSO :

Particle Swarm Optimization

SVM :

Support Vector Machine

LGBM :

Light Gradient Boosting Machine

NREL :

National Renewable Energy Laboratory

BMA :

Bayesian Model Averaging

ANFIS :

Adaptive Neuro Fuzzy Interface System

MVO :

Multiverse Optimization

FFA :

Firefly Algorithm

IGWO :

Improved Grey Wolf Optimization

SSA :

Salp Swarm Algorithm

WOA :

Whale Optimization Algorithm

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Jamshedpur, Jharkhand, India

    Mantosh Kumar & Kumari Namrata

  2. Electrical Engineering, B K Birla Institute of Engineering & Technology, Pilani, India

    Nishant Kumar

  3. Department of Mechanical Engineering, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, India

    Gaurav Saini

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  1. Mantosh Kumar
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  2. Kumari Namrata
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Contributions

Mantosh Kumar has collected and analysed the data along with computation and mathematical modelling for the methodology adopted. Kumari Namrata supervised the project and formatted the manuscript. Nishant Kumar and Gaurav Saini have worked in optimization technique formulation and also assisted in editing and formulating the manuscript.

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Correspondence to Kumari Namrata.

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Kumar, M., Namrata, K., Kumar, N. et al. Solar Irradiance Prediction Using an Optimized Data Driven Machine Learning Models. J Grid Computing 21, 28 (2023). https://doi.org/10.1007/s10723-023-09668-9

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