Статья

Multi-criteria decision-making for sound and vibration reduction platforms for financial and marketing optimization in energy

Alexey MikhaylovFinancial Department, Financial University under the Government of the Russian Federation, Moscow 125993, Russia; Department of Informatics, Plekhanov Russian University of Economics, Moscow 117997, Russia; Department of Science, Baku Eurasian University, Baku AZ 1073, AzerbaijanMurat Akramovich IkramovDepartment of Marketing, Tashkent State Economic University, Tashkent 100003, UzbekistanNilufar NabiyevaDepartment of International Tourism and Economics, Kokand University, Kokand 150700, UzbekistanBoris SokolovDepartment of Credit Theory and Financial Management, Faculty of Economics, Saint Petersburg State University, Saint Petersburg 199034,RussiaWenyi ZhangDepartment of Credit Theory and Financial Management, Faculty of Economics, Saint Petersburg State University, Saint Petersburg 199034,RussiaValentin NazarovDepartment of Marketing, Tashkent State Economic University, Tashkent 100003, UzbekistaMukhabbat ErgashevaDepartment of Marketing, Tashkent State Economic University, Tashkent 100003, UzbekistanSardar TuraevDepartment of Marketing, Tashkent State Economic University, Tashkent 100003, UzbekistanDilnoza MeilyevaDepartment of Marketing, Tashkent State Economic University, Tashkent 100003, UzbekistanDaria DinetsDepartment of Finance, Accounting, and Auditing, Peoples’ Friendship University of Russia named after Patrice Lumumba, Moscow 117198, RussiaYuri N. SotskovUnited Institute of Informatics ProblemsN. B. A. YousifDepartment of Sociology, College of Humanities and Science, Ajman University, Ajman P.O. Box 346, United Arab Emirates; Humanities and Social Sciences Research Centre (HSSRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates

2026

ABI

Аннотация

The integration of Artificial Intelligence (AI) in energy infrastructure has created a new class of specialized intermediaries for environmental control, yet their opaque decision-making poses regulatory challenges. This paper proposes a novel regulatory framework for specialized sound and vibration platform operators in the energy sector and introduces a multi-criteria decision-making (MCDM) methodology to support oversight. The methodology integrates expert neuro-behavioral data, captured via Facial Action Coding System (FACS), with a quantum picture fuzzy rough set extension and the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method. The application is demonstrated through a case study of a 250 MW combined-cycle gas turbine power plant, where the goal is to select optimal noise and vibration control technologies. The analysis assesses five key technologies against compliance parameters: algorithmic transparency, data governance, system reliability, operational accountability, and consumer protection. The proposed Neuro-Quantum Picture Fuzzy Rough MCDM model achieved a forecast accuracy of 0.987 for system performance, substantially outperforming Long Short-Term Memory (LSTM (0.876)), Recurrent Neural Network (RNN (0.575)), and AutoRegressive Integrated Moving Average (ARIMA (0.551)). The primary contribution is to initiate professional dialogue on governing AI-driven energy intermediaries, balancing technological innovation with energy stability, security, and consumer welfare. The paper recommends a comprehensive regulatory framework for a new class of energy intermediaries for financial and marketing optimisation called specialised sound and vibration platform operators.

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Идентификаторы

DOI: 10.59400/sv3941

Цитирования и источники

Цитирований: 1 Использованных источников: 107