Open AccessTakagi–Sugeno fuzzy model‐based approach considering multiple weather factors for the photovoltaic power short‐term forecasting
Author(s) -
Liu Fang,
Li Ranran,
Li Yong,
Yan Ruifeng,
Saha Tapan
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2016.1036
Subject(s) - photovoltaic system , computer science , fuzzy logic , probabilistic forecasting , support vector machine , wind speed , artificial neural network , cluster analysis , data mining , term (time) , wind power , hilbert–huang transform , artificial intelligence , meteorology , engineering , geography , physics , filter (signal processing) , quantum mechanics , probabilistic logic , electrical engineering , computer vision
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 433kW 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.