Investigation of applicability of impact factors to estimate solar irradiance: comparative analysis using machine learning algorithms

Jaehoon Cha, Moon Keun Kim*, Sanghyuk Lee, Kyeong Soo Kim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors.

Original languageEnglish
Article number8533
JournalApplied Sciences (Switzerland)
Issue number18
Publication statusPublished - Sept 2021


  • Artificial neural networks
  • Deducing modelling
  • Impact factors
  • Long short-term memory
  • Solar irradiance
  • Support vector machine

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