首页|Study Data from Kazan State Power Engineering University Update Knowledge of Mac hine Learning (Forecasting Peak Hours for Energy Consumption in Regional Power S ystems)
Study Data from Kazan State Power Engineering University Update Knowledge of Mac hine Learning (Forecasting Peak Hours for Energy Consumption in Regional Power S ystems)
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Current study results on artificial in telligence have been published. According to news reporting originating from Kaz an State Power Engineering University by NewsRx correspondents, research stated, ". Electrical power is the second most important commodity in electrical energy markets." The news journalists obtained a quote from the research from Kazan State Power E ngineering University: "For consumers, the charged amount of ‘generator' power i s determined as the average value of hourly consumption amounts on working days during peak hours in the region. The cost of power in some regions can reach 40 % of the final tariff, so reducing the load during peak hours by 1 0 % can lead to a decrease in monthly consumer payments by 3 % . However, such a way of saving money is not available to the consumer since the commercial operator of the wholesale market of electricity and capacity publish es the peak hours of the regions after the 10th day of the next month, when this information is no longer relevant. Timely forecasting of peak hours will make i t possible, on the one hand, to reduce consumer costs for payments for electric power, and on the other hand, to smooth out the daily schedule of electric load of the power system, thereby optimizing the operation of generating equipment of stations and networks of the system operator. The article presents a study of t he effectiveness of machine learning methods in the context of forecasting the p eak hour of a regional power system. The study concerns the period from November 2011 to October 2023, covers 76 regions of the Russian Federation, including su bjects of price (1st and 2nd) and non-price zones and includes 10 machine-learni ng methods. The results of the study showed that statistically, the K-nearest ne ighbors clustering method turns out to be the most accurate, although not univer sal."
Kazan State Power Engineering UniversityCyborgsEmerging TechnologiesMachine Learning
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