Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast
Wind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found...
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doaj-a726b6ccdc3e426dad831d6ab1ccb1382020-11-25T02:23:50ZengMDPI AGApplied Sciences2076-34172019-01-019342310.3390/app9030423app9030423Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed ForecastShenghui Zhang0Yuewei Liu1Jianzhou Wang2Chen Wang3School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, ChinaSchool of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, ChinaSchool of Statistics, Dongbei University of Finance and Economics, Dalian 116025, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaWind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found that the time series of wind speed not only has linear characteristics, but also nonlinear. In addition, most methods only consider one criterion or rule (stability or accuracy), or one objective function, which can lead to poor forecasting results. So, wind-speed forecasting is still a difficult and challenging problem. The existing forecasting models based on combination-model theory can adapt to some time-series data and overcome the shortcomings of the single model, which achieves poor accuracy and instability. In this paper, a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions and four models (two hybrid nonlinear models and two linear models) is proposed and was successfully applied to forecasting wind speed, which not only overcomes the issue of forecasting accuracy, but also solves the difficulties of forecasting stability. The experimental results show that the stability and accuracy of the proposed combined model are better than the single models, improving the mean absolute percentage error (MAPE) range from 0.007% to 2.31%, and the standard deviation mean absolute percentage error (STDMAPE) range from 0.0044 to 0.3497.https://www.mdpi.com/2076-3417/9/3/423multi-objective optimizationwind speed forecastingcombined model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shenghui Zhang Yuewei Liu Jianzhou Wang Chen Wang |
spellingShingle |
Shenghui Zhang Yuewei Liu Jianzhou Wang Chen Wang Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast Applied Sciences multi-objective optimization wind speed forecasting combined model |
author_facet |
Shenghui Zhang Yuewei Liu Jianzhou Wang Chen Wang |
author_sort |
Shenghui Zhang |
title |
Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast |
title_short |
Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast |
title_full |
Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast |
title_fullStr |
Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast |
title_full_unstemmed |
Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast |
title_sort |
research on combined model based on multi-objective optimization and application in wind speed forecast |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-01-01 |
description |
Wind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found that the time series of wind speed not only has linear characteristics, but also nonlinear. In addition, most methods only consider one criterion or rule (stability or accuracy), or one objective function, which can lead to poor forecasting results. So, wind-speed forecasting is still a difficult and challenging problem. The existing forecasting models based on combination-model theory can adapt to some time-series data and overcome the shortcomings of the single model, which achieves poor accuracy and instability. In this paper, a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions and four models (two hybrid nonlinear models and two linear models) is proposed and was successfully applied to forecasting wind speed, which not only overcomes the issue of forecasting accuracy, but also solves the difficulties of forecasting stability. The experimental results show that the stability and accuracy of the proposed combined model are better than the single models, improving the mean absolute percentage error (MAPE) range from 0.007% to 2.31%, and the standard deviation mean absolute percentage error (STDMAPE) range from 0.0044 to 0.3497. |
topic |
multi-objective optimization wind speed forecasting combined model |
url |
https://www.mdpi.com/2076-3417/9/3/423 |
work_keys_str_mv |
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