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Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting

The wind speed forecasting is important to observe the wind behaviour in the future and control the harms caused by high or slow speeds. Daily wind speed is more consistent and reliable than other time scales by providing vast monitoring and effective planning. Although a linear autoregressive integ...

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Main Author: Shukur, Osamah Basheer
Format: Thesis
Language:English
Published: 2015
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/54833/1/OsamahBasheerShukurPFS2015.pdf
http://eprints.utm.my/id/eprint/54833/
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spelling my.utm.548332017-10-08T09:06:31Z http://eprints.utm.my/id/eprint/54833/ Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting Shukur, Osamah Basheer QA Mathematics The wind speed forecasting is important to observe the wind behaviour in the future and control the harms caused by high or slow speeds. Daily wind speed is more consistent and reliable than other time scales by providing vast monitoring and effective planning. Although a linear autoregressive integrated moving average (ARIMA) model has been used for wind speed forecasting in many recent studies, but the model is unable to identify the nonlinear pattern of wind speed data. ARIMA modelling process causes the stochastic uncertainty as a second reason of inaccurate forecasting results. Wind speed data collection process faces several problems such as the failure of data observing devices or other casual problems that lead losing parts of data. Therefore, wind speed data naturally contains missing values. In this study, an ARIMA-artificial neural network (ANN) and ARIMA-Kalman filter (KF) methods are proposed to improve wind speed forecasting by handling the nonlinearity and the uncertainty respectively. A new hybrid KF-ANN method based on the ARIMA model improves the accuracy of wind speed forecasting by rectifying both nonlinearity and uncertainty jointly. These proposed methods are compared with others such as AR-ANN, AR-KF, and Zhang’s method. AR-ANN method is also used to impute the missing values. It is capable to overcome the missing values problem in wind speed data with nonlinear characteristic. It is compared with linear, nearest neighbour, and state space methods. Two different daily wind speed data from Iraq and Malaysia have been used as case studies. The forecasting results of the ARIMA-ANN, ARIMA-KF and the new hybrid KF-ANN methods have shown in better forecasting than other compared methods, while AR-KF and AR-ANN methods provided acceptable forecasts compared to ARIMA model. The ARIMAANN and the new hybrid KF-ANN methods outperformed all other methods. The comparison of missing values imputation methods has shown that AR-ANN outperformed the others. In conclusion, the ARIMA-ANN and the new hybrid KFANN can be used to forecast wind speed data with nonlinearity and uncertainty characteristics more accurately. The imputation method AR-ANN can be used to impute the missing values accurately in wind speed data with nonlinear characteristic. 2015-09 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/54833/1/OsamahBasheerShukurPFS2015.pdf Shukur, Osamah Basheer (2015) Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting. PhD thesis, Universiti Teknologi Malaysia, Faculty of Science.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Shukur, Osamah Basheer
Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
description The wind speed forecasting is important to observe the wind behaviour in the future and control the harms caused by high or slow speeds. Daily wind speed is more consistent and reliable than other time scales by providing vast monitoring and effective planning. Although a linear autoregressive integrated moving average (ARIMA) model has been used for wind speed forecasting in many recent studies, but the model is unable to identify the nonlinear pattern of wind speed data. ARIMA modelling process causes the stochastic uncertainty as a second reason of inaccurate forecasting results. Wind speed data collection process faces several problems such as the failure of data observing devices or other casual problems that lead losing parts of data. Therefore, wind speed data naturally contains missing values. In this study, an ARIMA-artificial neural network (ANN) and ARIMA-Kalman filter (KF) methods are proposed to improve wind speed forecasting by handling the nonlinearity and the uncertainty respectively. A new hybrid KF-ANN method based on the ARIMA model improves the accuracy of wind speed forecasting by rectifying both nonlinearity and uncertainty jointly. These proposed methods are compared with others such as AR-ANN, AR-KF, and Zhang’s method. AR-ANN method is also used to impute the missing values. It is capable to overcome the missing values problem in wind speed data with nonlinear characteristic. It is compared with linear, nearest neighbour, and state space methods. Two different daily wind speed data from Iraq and Malaysia have been used as case studies. The forecasting results of the ARIMA-ANN, ARIMA-KF and the new hybrid KF-ANN methods have shown in better forecasting than other compared methods, while AR-KF and AR-ANN methods provided acceptable forecasts compared to ARIMA model. The ARIMAANN and the new hybrid KF-ANN methods outperformed all other methods. The comparison of missing values imputation methods has shown that AR-ANN outperformed the others. In conclusion, the ARIMA-ANN and the new hybrid KFANN can be used to forecast wind speed data with nonlinearity and uncertainty characteristics more accurately. The imputation method AR-ANN can be used to impute the missing values accurately in wind speed data with nonlinear characteristic.
format Thesis
author Shukur, Osamah Basheer
author_facet Shukur, Osamah Basheer
author_sort Shukur, Osamah Basheer
title Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
title_short Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
title_full Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
title_fullStr Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
title_full_unstemmed Artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
title_sort artificial neural network and kalman filter approaches based on arima for daily wind speed forecasting
publishDate 2015
url http://eprints.utm.my/id/eprint/54833/1/OsamahBasheerShukurPFS2015.pdf
http://eprints.utm.my/id/eprint/54833/
_version_ 1643653619533217792
score 13.211869

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