Machine generated tools for short-term forecasting of price of electrical energy

In the contemporary context of energy markets, short-term electricity price forecasting plays a critical role for the efficient management of resources and optimization of energy systems. This paper explores the application of artificial intelligence and machine learning as tools for generating accurate short-term electricity price forecasts. We consider different algorithms, including neural networks, support vector machine learning, and time series, and analyze their accuracy and effectiveness in different real-world market conditions. The aim of the study is to provide a scientifically based view on the potential of machine-generated tools to improve the accuracy and reliability of short-term energy forecasts, which can contribute to greater stability and optimization of energy markets.