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

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.

Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations

“…When choosing a microprocessor, the intensity of the operations to be carried out, the operating voltage and current values of the sensors, if any, the adequacy of the communication ports, and the number of timers are important. For the controls to be carried out properly, it is important to choose a processor that works more stable than other processors and can process a large amount of data at the same time [28].…”

“…Since the supply voltages of the op amp to be used are +12 V and −12 V, the RB0512 DC/DC converter module, which provides voltage regulation from 5 V to these values, was used. The PWM signal generated by the processor is compared with the CP signal coming from the BMS [26][27][28]. Here, the duty cycle ratio of the PWM signal will determine the charging current.…”