Pattern-based Short-Term Load Forecasting using Optimized ANFIS with Cuckoo Search Algorithm

被引：0

作者：

Mustapha, Mamunu ^{[1
]}

Salisu, Sani ^{[2
]}

Ibrahim, Abdullahi Abdu ^{[3
]}

Almustapha, Muhammad Dikko ^{[4
]}

机构：

[1] Kano Univ Sci & Technol Wudil, Dept Elect Engn, Kano, Nigeria

[2] Ahmadu Bello Univ Zaria, Dept Elect Engn, Kaduna, Nigeria

[3] Altinbas Univ, Dept Comp Engn, Istanbul, Turkey

[4] Ahmadu Bello Univ Zaria, Dept Elect & Commun Engn, Kaduna, Nigeria

来源：

2ND INTERNATIONAL CONGRESS ON HUMAN-COMPUTER INTERACTION, OPTIMIZATION AND ROBOTIC APPLICATIONS (HORA 2020) | 2020年

关键词：

Load Forecasting; Cuckoo Search Algorithm; ANFIS; Pattern-based; Optimization; PARAMETERS;

D O I：

10.1109/hora49412.2020.9152879

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Accurate short-term load forecasting (STLF) depends on proper data selection and model development. The research addresses the problem of data selection based on the energy consumption pattern using correlation analysis and hypothesis test. It also employed the use of Cuckoo Search Optimization algorithm (CSO) to improve Adaptive Network-based Fuzzy Inference System (ANFIS) by replacing the Gradient Descent (GD) algorithm in the backward pass of the classical ANFIS model. The aim is to improve the forecasting error and enhance the forecasting time. Based on the conducted experiment CSO parameters for optimal performance of ANFIS were determined and utilized. Based on the results obtained it is observed that CSO-ANFIS with proposed data selection produced low Root Means Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).

引用

页码：42 / 48

页数：7

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