Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Mehdi Rafiei (Author) Other; Taher Niknam (Author) Other; Jamshid Aghaei (Author) Other; Miadreza Shafie-Khah (Author) Other; João P. S. Catalão (Author) FEUP

Journal

Title: IEEE Transactions on Smart Grid

Vol. 9 No. 6

Pages: 6961-6971

ISSN: 1949-3053

Publisher: IEEE

Indexing

ISI Web of Knowledge - 101 Citations

Scopus - 136 Citations

INSPEC

Other information

Authenticus ID: P-00P-R06

DOI: 10.1109/tsg.2018.2807845

Abstract (EN): Competitive transactions resulting from recent restructuring of the electricity market, have made achieving a precise and reliable load forecasting, especially probabilistic load forecasting, an important topic. Hence, this paper presents a novel hybrid method of probabilistic electricity load forecasting, including generalized extreme learning machine fin- training an improved wavelet neural network, wavelet preprocessing and bootstrapping. In the proposed method, the forecasting model and data noise uncertainties are taken into account while the output of the model is the load probabilistic interval. In order to validate the method, it is implemented on the Ontario and Australian electricity markets data. Also, in order to remove the influence of model parameters and data on performance validation, Friedman and post-hoc tests, which are non-parametric tests, are applied to the proposed method. The results demonstrate the high performance, accuracy, and reliability of the proposed method.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11