Forecasting Photovoltaic Output Power Based on Environmental Parameters Using Artificial Neural Network Methods

Forecasting Photovoltaic Output Power Based on Environmental Parameters Using Artificial Neural Network Methods

artificial neural network; forecasting; output power; photovoltaic

Photovoltaic is a system that can convert sunlight into electrical energy. However, photovoltaic efficiency tends to be low and
its performance is affected by several environmental parameters such as dust, wind speed, humidity, temperature and other
external factors. Because many factors can affect the power generated, we need a power output prediction system that can
assist in planning and managing as well as increasing the efficiency of photovoltaic systems. In this research, a system is
designed that can predict the photovoltaic output power in the short term using the Artificial Neural Network method or what
is often called an artificial neural network. Predictions are made based on the effects of several environmental parameters
such as wind speed, dust, humidity, and temperature on a 10 Wp photovoltaic system. Performance data for 7 days is used as
a dataset and then processed using ANN with 1 input layer, 3 hidden layers 1 output layer and 3 sample epochs (10, 100, and
1000). The results of the study can predict the output of photovoltaic power for the next 4 days with an error value of Mean
Square Error (MSE) of 0.0010, Mean Absolute Error (MAE) of 0.0155, Root Mean Square Error (RMSE) of 0.0229 with an increase in power reach 0.5 to 1 watt.

Desri Kristina Silalahi, Agnes Christy Margareth Rumapea, Wahmisari Priharti, Bandiyah Sri Aprillia

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

December 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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