NDLI: Takagi–Sugeno fuzzy model-based approach considering multiple weather factors for the photovoltaic power short-term forecasting

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Takagi–Sugeno fuzzy model-based approach considering multiple weather factors for the photovoltaic power short-term forecasting

Content Provider	IET Digital Library
Author	Liu, Fang Li, Ranran Li, Yong Yan, Ruifeng Saha, Tapan Kumar
Abstract	With the increasing contribution of the power production by the photovoltaic (PV) systems to the electricity supply, the PV power forecasting becomes increasingly important. There are many factors influencing the forecasting performance, such as the air temperature, humidity, insolation, wind speed, wind direction and so on. This study proposes a Takagi–Sugeno (T–S) fuzzy model-based PV power short-term forecasting approach. First, by means of the correlation analysis, the influential factors are selected as the model inputs. Then, the fuzzy c-mean clustering algorithm and the recursive least squares method are used to identify the antecedent and the consequent parameters. The performance of the proposed forecasting approach is tested by using a large database of measurement data from the 433 kW PV array at St Lucia campus of The Queensland University of Australia. The forecasting results are compared with the support vector machine (SVM), the hybrid of empirical mode decomposition and SVM, the back propagation neural network and the recurrent neural network. The results indicate that, compared with the existing approaches, the proposed T–S fuzzy model-based forecasting approach is simpler and can forecast more accurately.
Starting Page	1281
Ending Page	1287
Page Count	7
ISSN	17521416
Volume Number	11
e-ISSN	17521424
Issue Number	Issue 10, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/11/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2016.1036
Journal	IET Renewable Power Generation
Publisher Date	2017-04-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Air Temperature Back Propagation Neural Network Backpropagation Correlation Analysis Electricity Supply Empirical Mode Decomposition Fuzzy C-mean Clustering Algorithm Fuzzy Reasoning Humidity Insolation Interpolation And Function Approximation Knowledge Engineering Technique Least Mean Squares Method Load Forecasting Multiple-weather Factors Neural Computing Technique Numerical Analysis Photovoltaic Power Short-term Forecasting Photovoltaic Power System Power 433 KW Power Engineering Computing Power System Planning And Layout PV Power Forecasting PV System Recurrent Neural Nets Recurrent Neural Network Recursive Estimation Recursive Least Square Method Solar Power Stations St Lucia Campus Statistics Support Vector Machine SVM T-S Fuzzy Model Takagi-Sugeno Fuzzy Model-based Approach The Queensland University of Australia Wind Direction Wind Speed
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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