首页|Ural Federal University Named after the First President of Russia B.N. Yeltsin R esearchers Describe Findings in Machine Learning (Application of SHAP and Multi- Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry ...)
Ural Federal University Named after the First President of Russia B.N. Yeltsin R esearchers Describe Findings in Machine Learning (Application of SHAP and Multi- Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news originating from Ekaterinburg, Russia, by NewsRx correspondents, research stated, "Currently, machine learning methods are widely applied in the power industry to solve various tasks, includ ing short-term power consumption forecasting." Funders for this research include Ministry of Science And Higher Education of Th e Russian Federation. The news editors obtained a quote from the research from Ural Federal University Named after the First President of Russia B.N. Yeltsin: "However, the lack of i nterpretability of machine learning methods can lead to their incorrect use, pot entially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of t he tasks of enhancing the energy efficiency of gas industry enterprises. In orde r to reduce the risks of making incorrect decisions based on the results of shor t-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a m ulti-agent approach for the decomposition of production processes using self-gen eration agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjus ting the operation modes of self-generation units and energy-storage systems, op timizing the power consumption schedule, and reducing electricity and power cost s. A comparative analysis of various algorithms for constructing decision tree e nsembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features."
Ural Federal University Named after the First President of Russia B.N. YeltsinEkaterinburgRussiaEurasiaCyborgsEmerging TechnologiesMachine Learning
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