Open Access

ARTICLE

Performance Analysis of Various Forecasting Models for Multi-Seasonal Global Horizontal Irradiance Forecasting Using the India Region Dataset

Manoharan Madhiarasan^*

Department of Business Development and Technology, Aarhus School of Business and Social Sciences (BSS), Aarhus University, Birk Centerpark 15, Herning, 7400, Denmark

* Corresponding Authors: Manoharan Madhiarasan. Email: or

(This article belongs to the Special Issue: Innovative Energy Engineering for Resilient and Green Systems)

Energy Engineering 2025, 122(8), 2993-3011. https://doi.org/10.32604/ee.2025.068358

Received 27 May 2025; Accepted 26 June 2025; Issue published 24 July 2025

Abstract

Accurate Global Horizontal Irradiance (GHI) forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources. Particularly considering the implications of the aggressive GHG emission targets, accurate GHI forecasting has become vital for developing, designing, and operational managing solar energy systems. This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA (Autoregressive Integrated Moving Average), Elaman NN (Elman Neural Network), RBFN (Radial Basis Function Neural Network), SVM (Support Vector Machine), LSTM (Long Short-Term Memory), Persistent, BPN (Back Propagation Neural Network), MLP (Multilayer Perceptron Neural Network), RF (Random Forest), and XGBoost (eXtreme Gradient Boosting) for assessing multi-seasonal forecasting of GHI. Used the India region data to evaluate the models’ performance and forecasting ability. Research using forecasting models for seasonal Global Horizontal Irradiance (GHI) forecasting in winter, spring, summer, monsoon, and autumn. Substantiated performance effectiveness through evaluation metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²), coded using Python programming. The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting, are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE: 1.6325, RMSE: 4.8338, and R²: 0.9998. Spring season-based forecasting XGBoost is the best forecasting model with MAE: 2.599599, RMSE: 5.58539, and R²: 0.999784. Summer season-based forecasting RF is the best forecasting model with MAE: 1.03843, RMSE: 2.116325, and R²: 0.999967. Monsoon season-based forecasting RF is the best forecasting model with MAE: 0.892385, RMSE: 2.417587, and R²: 0.999942. Autumn season-based forecasting RF is the best forecasting model with MAE: 0.810462, RMSE: 1.928215, and R²: 0.999958. Based on seasonal variations and computing constraints, the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.

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