Multi-objective algorithm for the design of prediction intervals for wind power forecasting model

A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-proc...

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Bibliographic Details
Published in:Applied Mathematical Modelling Vol. 67; p. 101
Main Authors: Jiang, Ping, Li, Ranran, Li, Hongmin
Format: Journal Article
Language:English
Published: New York Elsevier BV 01.03.2019
Subjects:
Algorithms
Datasets
Electric power generation
Feature extraction
Forecasting
Fuzzy set theory
Fuzzy sets
Intervals
Machine learning
Multiple objective analysis
Pareto optimization
Variation
Wind power
Wind power generation
Wind speed
ISSN:1088-8691, 0307-904X
Online Access:Get full text
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Summary:A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width.
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ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2018.10.019