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A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China
Author(s) -
Peng Zhang,
Xin Ma,
Kun She
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0225362
Subject(s) - wind power , energy consumption , renewable energy , computer science , mathematical optimization , consumption (sociology) , heuristic , exponential growth , macro , wind power forecasting , environmental science , environmental economics , operations research , power (physics) , mathematics , artificial intelligence , electric power system , engineering , economics , electrical engineering , mathematical analysis , social science , physics , quantum mechanics , sociology , programming language
Wind energy is one of the most important renewable resources and plays a vital role in reducing carbon emission and solving global warming problem. Every country has made a corresponding energy policy to stimulate wind energy industry development based on wind energy production, consumption, and distribution. In this paper, we focus on forecasting wind energy consumption from a macro perspective. A novel power-driven fractional accumulated grey model (PFAGM) is proposed to solve the wind energy consumption prediction problem with historic annual consumption of the past ten years. PFAGM model optimizes the grey input of the classic fractional grey model with an exponential term of time. For boosting prediction performance, a heuristic intelligent algorithm WOA is used to search the optimal order of PFAGM model. Its linear parameters are estimated by using the least-square method. Then validation experiments on real-life data sets have been conducted to verify the superior prediction accuracy of PFAGM model compared with other three well-known grey models. Finally, the PFAGM model is applied to predict China’s wind energy consumption in the next three years.

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