TY - JOUR
T1 - Modelling of photovoltaic system power prediction based on environmental conditions using neural network single and multiple hidden layers
AU - Azka, R.
AU - Soefian, W.
AU - Aryani, D. R.
AU - Jufri, F. H.
AU - Utomo, A. R.
N1 - Funding Information:
This research is supported by research grant of PUTI (Publikasi Terindeks International) Prosiding 2020 from Universitas Indonesia (UI).
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/24
Y1 - 2020/11/24
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097522546&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/599/1/012032
DO - 10.1088/1755-1315/599/1/012032
M3 - Conference article
AN - SCOPUS:85097522546
SN - 1755-1307
VL - 599
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012032
T2 - 2nd International Conference on Green Energy and Environment, ICoGEE 2020
Y2 - 8 October 2020
ER -
