Modelling of photovoltaic system power prediction based on environmental conditions using neural network single and multiple hidden layers

R. Azka, W. Soefian, D. R. Aryani, F. H. Jufri, A. R. Utomo

Research output: Contribution to journalConference articlepeer-review

Abstract

The solar power plant is an alternative to the provision of environmentally friendly renewable electricity, especially in the tropics, which are sufficiently exposed to the sun throughout the year. However, environmental conditions such as rainfall, solar radiation, or clouds may affect the output power of photovoltaic (PV) systems. These factors make it difficult to know whether PV can meet the needs of the existing load. This research develops a model to predict the output power of a 160 x 285W PV system located in the tropics and has certain environmental conditions. The prediction development is supported by the Python programming language with a single hidden layer and two hidden layers Neural Network, as well as the traditional Multiple Linear Regression tools. The simulation results show that the two hidden layers Neural Network method has a higher level of accuracy compared to the single hidden layer and Multiple Linear Regression as seen from the value of R2, MSE, and MAE.

Original languageEnglish
Article number012032
JournalIOP Conference Series: Earth and Environmental Science
Volume599
Issue number1
DOIs
Publication statusPublished - 24 Nov 2020
Event2nd International Conference on Green Energy and Environment, ICoGEE 2020 - Pangkalpinang, Indonesia
Duration: 8 Oct 2020 → …

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