Title of Paper:Seasonal electricity consumption forecasting: an approach with novel weakening buffer operator and fractional order accumulation grey model

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Affiliation of Author(s):Nanjing University of Aeronautics and Astronautics, College of Economics and Management

Teaching and Research Group:管理科学与工程

Journal:Grey Systems: Theory and Application

Key Words:Electricity consumption; Seasonal grey prediction modeling; Grey buffer operator; Long timescale flexibility analysis

Abstract:Purpose – Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.
Design/methodology/approach – First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientific and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.
Findings – Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.
Originality/value – Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

Discipline:Management Science

Document Type:J

Volume:14

Issue:2

Page Number:414-428

Translation or Not:no

Date of Publication:2024-02-28

Included Journals:SCI

Co-author:Yatng Ren,Bo Zeng,Jamshed Khalid

Correspondence Author:Xiaoyi Gou