Akademik Veri Yönetim Sistemi
13th International Symposium on Intelligent Manufacturing and Service Systems, Düzce, Türkiye, 25 - 27 Eylül 2025, ss.1-8, (Tam Metin Bildiri)
The utilization of solar energy diminishes the
reliance on non-renewable fossil fuels and mitigates climate change
consequences by lowering carbon emissions. Photovoltaic systems are among the
most used technologies for transforming solar energy into useful electrical
energy. However, power generation in solar systems varies due to numerous
system-related factors. Therefore, the estimation of photovoltaic energy is
important in terms of management, planning, integration and sustainability. Additionally,
manual solar energy forecasting requires significant human effort and expertise
to analyse and interpret the data. This process is time-consuming and open to
errors. Thus, recently, the researchers around the world have been focusing on
the application of artificial intelligence and machine learning approaches to
forecast power production in the highly unpredictable renewable energy sector.
In this study, the performances of the derivatives of long-short term memory
(LSTM) neural networks are investigated in one hour ahead forecasting of the
solar power output for a solar farm located in Türkiye. The aim of the study is
to determine the best performing LSTM derivative for this problem and
demonstrate the application of the algorithms in a specific case study, where
the utilized data is publicly available. Thus, the methodology presented here
can be utilized by anyone, including government agencies, to forecast solar
power output of any solar farm, making this study a significant contribution to
the existing literature.