A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

Derya Betul Unsal (), Ahmet Aksoz, Saadin Oyucu, Josep M. Guerrero and Merve Guler
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Derya Betul Unsal: Department of Electrical and Electronics Engineering, Cumhuriyet University, Sivas 58140, Turkey
Ahmet Aksoz: MOBILERS Research Team, Sivas Cumhuriyet University, Sivas 58580, Turkey
Saadin Oyucu: Department of Computer Engineering, Adıyaman University, Adıyaman 02040, Turkey
Josep M. Guerrero: Department of Electronic Engineering (EEL), Universitat Politècnica de Catalunya (UPC), 08019 Barcelona, Spain
Merve Guler: Renewable Energy Research Center, Cumhuriyet University, Sivas 58140, Turkey

Sustainability, 2024, vol. 16, issue 7, 1-26

Abstract: Fossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey’s energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system.

Keywords: renewable energy; smart grid; deep learning; ANN; LSTM; CNN; LightGBM; GBR; RF (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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