Akademik Veri Yönetim Sistemi
Journal of Modern Technology and Engineering, cilt.3, sa.8, ss.165-188, 2023 (Hakemli Dergi)
Countries’ ambition to achieve independence from foreign energy sources, coupled with the need for
future energy production forecasts based on reliable information, not only enables the safe operation of electrical
networks, but also enhances the economic efficiency of these systems designed to utilize energy resources. Therefore, the prediction of energy production from renewable energy sources has emerged as a highly researched topic
of considerable interest. Deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent
Units (GRU), and One-Dimensional Convolutional Neural Networks (1D-CNN), have been demonstrated efficacy
in diverse forecasting tasks, including economic time series and computer vision. However, their application to
energy production forecasting from renewable energy plants has only recently seen a significant surge. This study
examines LSTM, GRU, and 1D-CNN based time-series forecasting experiments for predicting solar power generation in ˙Izmir, the third largest city in T¨urkiye. The predictions have undergone comparative analysis using
various statistical calculations, and the results are depicted visually through graphs. The primary objective of
these computations is to deliver an optimized academic outcome, potentially necessary for the development of
new solar energy fields. This could significantly contribute to the amplified usage of solar energy, a sustainable
and cleaner energy source, in T¨urkiye.