Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions

作     者：Muhammad Hamza Azam Mohd Hilmi Hasan Azlinda A Malik Saima Hassan Said Jadid Abdulkadir 

作者机构：Centre of Research in Data ScienceComputerand Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Petroleum Engineering DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Institute of ComputingKohat University of Science and TechnologyKohat26000K.P.KPakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：1799-1815页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：This research is an ongoing research supported by Yayasan UTP Grant(015LC0-321&015LC0-311) Fundamental Research Grant Scheme(FRGS/1/2018/ICT02/UTP/02/1) a grant funded by the Ministry of Higher Education,Malaysia 

主　　题：Fuzzy logic fuzzy C-means type-1 fuzzy membership function electricity price forecasting 

摘      要：Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future *** price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 *** strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity *** c-means(FCM)is one of the popular clustering approach for generating fuzzy membership ***,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian *** generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem *** a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is ***,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM *** approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is *** results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted.