Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models

Rabia Sultan Yıldırım

doi:10.5152/tepes.2026.25039

Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models

Abstract

This study presents a comprehensive framework for short-term solar irradiance forecasting in the Adana Organized Industrial Zone, one of Türkiye’s most solarrich regions. High-resolution irradiance data collected throughout 2024 using ISO 9060–compliant pyranometers were analyzed to capture seasonal and daily variations, with annual cumulative global horizontal irradiance measured at ~1346 kWh/m2. Two machine learning approaches were applied to predict hourly irradiance: Random Forest (RF) regression and multilayer perceptron (MLP) neural networks. Input features included persistence-based irradiance values, diurnal and seasonal indicators, and time-series variables. Model performance was evaluated using mean absolute error, root mean square error (RMSE), and R2 metrics. Results indicated that RF achieved superior accuracy (R2 = 0.891, RMSE = 0.472 kWh/m2) compared to MLP (R2 = 0.874, RMSE = 0.514 kWh/m2), highlighting the robustness of ensemble methods for short-term forecasting. Uncertainty analysis confirmed that measurement errors and cloudy-day conditions remain key challenges. Unlike previous studies, this work provides one of the first high-resolution, region-specific datasets for industrial-scale solar forecasting in Türkiye. Overall, the findings demonstrate that data-driven forecasting can effectively support photovoltaic (PV) system operation, smart grid integration, and regional energy planning, providing valuable insights for the sustainable energy transition in Türkiye.

Keywords

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Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Authors

Rabia Sultan Yıldırım ^*
0000-0001-9281-9504
Türkiye

Publication Date

February 27, 2026

Submission Date

October 12, 2025

Acceptance Date

November 19, 2025

Published in Issue

Year 2026 Volume: 6 Number: 1

DOI

https://doi.org/10.5152/tepes.2026.25039

IZ

https://izlik.org/JA93PY53DL

Cite

RIS / Bibtex

APA

Yıldırım, R. S. (2026). Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models. Turkish Journal of Electrical Power and Energy Systems, 6(1), 48-58. https://doi.org/10.5152/tepes.2026.25039

AMA

1.Yıldırım RS. Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models. TEPES. 2026;6(1):48-58. doi:10.5152/tepes.2026.25039

Chicago

Yıldırım, Rabia Sultan. 2026. “Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models”. Turkish Journal of Electrical Power and Energy Systems 6 (1): 48-58. https://doi.org/10.5152/tepes.2026.25039.

EndNote

Yıldırım RS (February 1, 2026) Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models. Turkish Journal of Electrical Power and Energy Systems 6 1 48–58.

IEEE

[1]R. S. Yıldırım, “Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models”, TEPES, vol. 6, no. 1, pp. 48–58, Feb. 2026, doi: 10.5152/tepes.2026.25039.

ISNAD

Yıldırım, Rabia Sultan. “Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models”. Turkish Journal of Electrical Power and Energy Systems 6/1 (February 1, 2026): 48-58. https://doi.org/10.5152/tepes.2026.25039.

JAMA

1.Yıldırım RS. Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models. TEPES. 2026;6:48–58.

MLA

Yıldırım, Rabia Sultan. “Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models”. Turkish Journal of Electrical Power and Energy Systems, vol. 6, no. 1, Feb. 2026, pp. 48-58, doi:10.5152/tepes.2026.25039.

Vancouver

1.Rabia Sultan Yıldırım. Machine Learning Approaches to Short-Term Solar Irradiance Forecasting: Performance Comparison of Random Forest and Multilayer Perceptron Models. TEPES. 2026 Feb. 1;6(1):48-5. doi:10.5152/tepes.2026.25039