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Cascade-Forward, Multi-Parameter Artificial Neural Networks for Predicting the Energy Efficiency of Photovoltaic Modules in Temperate Climate


[ 1 ] Instytut Elektrotechniki i Elektroniki Przemysłowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technology

Year of publication


Published in

Applied Sciences

Journal year: 2024 | Journal volume: vol. 14 | Journal number: iss. 7

Article type

scientific article

Publication language


  • ANN
  • PV
  • solar
  • renewable energy
  • modeling
  • case study

EN Solar energy is a promising and efficient source of electricity in countries with stable and high sunshine duration. However, in less favorable conditions, for example in continental, temperate climates, the process requires optimization to be cost-effective. This cannot be done without the support of appropriate mathematical and numerical methods. This work presents a procedure for the construction and optimization of an artificial neural network (ANN), along with an example of its practical application under the conditions mentioned above. In the study, data gathered from a photovoltaic system in 457 consecutive days were utilized. The data includes measurements of generated power, as well as meteorological records. The cascade-forward ANN was trained with a resilient backpropagation procedure and sum squared error as a performance function. The final ANN has two hidden layers with nine and six nodes. This resulted in a relative error of 10.78% and R2 of 0.92–0.97 depending on the data sample. The case study was used to present an example of the potential application of the tool. This approach proved the real benefits of the optimization of energy consumption.

Date of online publication


Pages (from - to)

2764-1 - 2764-16





Article number: 2764

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final author's version

Release date


Date of Open Access to the publication

at the time of publication

Full text of article

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Ministry points / journal


Impact Factor

2,5 [List 2023]

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