Prediction of energy balance by algorithm

The Turkish team tested different machine learning algorithms to predict electricity demand from different power sources. They trained an algorithm on electricity demand data from 2000 to 2022 and used them to successfully make his 2023 predictions with varying degrees of accuracy.

Researchers tested the predictive power of long short-term memory (LSTM), artificial neural networks (ANN), linear regression (LR), support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR) Did. and Extreme Gradient Boosting (XGBoost) and demonstrated that LSTM is the most accurate. Such algorithms can be used to model energy usage and production for long-term power planning around the world.

write to International Journal of Oil, Gas and Coal Technology, Mehmet Hakan Ozdemir and Batin Latif Airak of the Turkish-German University in Istanbul, Murat Ince of the Isparta University of Applied Sciences, and Okan Oral of Akdeniz University in Antalya, Turkey, discuss supply and demand. suggests understanding it from a different perspective. At this point in human history, non-renewable and renewable energy sources are extremely important.

Given that non-renewable resources such as fossil fuels are finite and irreplaceable, replenishable renewable resources such as wind, solar, hydropower, geothermal and biogas are becoming a priority for power generation. I am. Machine learning has the ability to identify complex relationships and patterns from large amounts of data, providing a powerful and flexible predictive approach.

In contrast to traditional statistical methods, machine learning algorithms trained on appropriate datasets can consider the totality of available data and identify conclusions about complex interactions that cannot be reached using traditional analytical methods. .

Machine learning could therefore help inform energy policy decisions and put the power generation industry on the path to a more sustainable future. Insights from the research demonstrate how machine learning algorithms can play a transformative role in not only informing decision makers but also redefining how these types of problems are solved. Make it embossed.