Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models

Hussin, Nazimah; Yusof, Fadhilah; Jamaludin, Aaishah Radziah; Norrulashikin, Siti Mariam

doi:10.47836/pjst.29.1.02

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…Following the interpretation for the typical MAPE value which was explained in [15], the error measures confirm the satisfactory performance of SARIMA models , MAPE values are between 11% and 20% means that the model offers good accuracy forecast . Moreover, compared to several wind speed prediction models cited in the literature based on the classical ARIMA method, the present SARIMA model provides good accuracy values of MAPE comparing to results observed in the bibliography, for one hour ahead forecast [16] [17] [18]. Furthermore, we notice that in march, we recorded the lowest MSE, RMSE and MAE errors, the Seasonal ARIMA model is more appropriate for periods with high velocity.…”

Section: Results Analysissupporting

confidence: 72%

Wind Speed Prediction Based on Seasonal ARIMA model

et al. 2022

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Major dependency on fossil energy resources and emission of greenhouse gases are common problems that have a very harmful impact on human communities. Thus, the use of renewable energy resources, such as wind power, has become a strong alternative to solve this problem. Nevertheless, because of the intermittence and unpredictability of the wind energy, an accurate wind speed forecasting is a very challenging research subject. This paper addresses a short-term wind speed forecasting based on Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The forecasting performances of the model were conducted using the same dataset under different evaluation metrics in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) performance evaluation metrics. The obtained results denote that the used model achieves excellent forecasting accuracy.

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Section: Results Analysissupporting

confidence: 72%

Wind Speed Prediction Based on Seasonal ARIMA model

et al. 2022

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“…The white noise test of residuals was performed using the Box.test function [ 11 ]. The Lagrange multiplier (LM) test of residuals was performed using the ArchTest function [ 21 ]. The model passed the above test ( p > 0.05), indicating that the model was applicable to the time series of the number of people with diabetes.…”

Section: Arima Modelmentioning

confidence: 99%

“…The white noise test of residuals was performed using the Box.test function [11]. The Lagrange multiplier (LM) test of residuals was performed using the ArchTest function [21].…”

Section: Modeling Processmentioning

confidence: 99%

Predicting Diabetes and Estimating Its Economic Burden in China Using Autoregressive Integrated Moving Average Model

Zhu

Zhou

et al. 2022

Int J Public Health

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Objectives: To predict the number of people with diabetes and estimate the economic burden in China.Methods: Data from natural logarithmic transformation of the number of people with diabetes in China from 2000 to 2018 were selected to fit the autoregressive integrated moving average (ARIMA) model, and 2019 data were used to test it. The bottom-up and human capital approaches were chosen to estimate the direct and indirect economic burden of diabetes respectively.Results: The number of people with diabetes in China would increase in the future. The ARIMA model fitted and predicted well. The number of people with diabetes from 2020 to 2025 would be about 94, 96, 97, 98, 99 and 100 m respectively. The economic burden of diabetes from 2019 to 2025 would be about $156b, $160b, $163b, $165b, $167b, $169b and $170b respectively.Conclusion: The situation of diabetes in China is serious. The ARIMA model can be used to predict the number of people with diabetes. We should allocate health resources in a rational manner to improve the prevention and control of diabetes.

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“…Hussin, Yusuf, and Norrulashikin, [18], used the Autoregressive Integrated Moving Average (ARIMA) and ARIMA-GARCH models to forecast future wind speed series. The Ljung-Box test was used to determine the presence of serial autocorrelation, while the Engle's Lagrange Multiplier (LM) test was used to investigate the presence of Autoregressive Conditional Heteroscedasticity (ARCH) effect in the residual of the ARIMA model.…”

Section: Related Workmentioning

confidence: 99%

An Application of Artificial Neural Network for Wind Speeds and Directions Forecasts in Airports

Lawrence¹,

Garba

Malgwi

et al. 2022

EJECE

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Wind speed patterns are highly dynamic and non-linear and thus cannot be accurately forecasted using conventional linear regression models. In this work, Artificial Neural Network (ANN) technique was applied to forecast wind speeds and directions in airports. Monthly data of maximum temperature, minimum temperature, wind speed, wind direction, relative humidity and wind run for Yola International Airport were collected from 1995 to 2021 from Nigerian Meteorological Agency (NIMET) Abuja-Nigeria. Six Neural Network models were built. ANN with no hidden layers, ANN model with one hidden layer and two dropout layers, ANN model with four hidden layers and three dropout layers, ANN model with eight hidden layers, ANN model with nine hidden layers and finally, ANN model with ten hidden layers. Back Propagation training algorithm was implemented using the PYTHON toolbox. Each of the models was trained using the training dataset and validated using the validation dataset. To test the forecasting ability of each of the models we tested it using unknown data that is the test dataset. The results from each of the models were organized and assessed in terms of the magnitude of the statistical error between the measured result and the real data. This was achieved by measuring the average of the Mean Square Errors (MSE) and Mean Absolute Error (MAE) for each of the models used for forecasting both wind speeds and directions. The results show that Multilayer perceptron with ten hidden layers with (MSE) = 0.92 and (MAE) = 0.73 emerged as the most preferred model for wind speeds forecast while the multilayer perceptron with four hidden layers with (MSE) = 1,858 and (MAE) = 35 emerged the most preferred model for wind directions forecast. Future research can be carried out to improve the accuracy of the model for wind direction forecasts.

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Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models

Cited by 16 publications

References 22 publications

Wind Speed Prediction Based on Seasonal ARIMA model

Wind Speed Prediction Based on Seasonal ARIMA model

Predicting Diabetes and Estimating Its Economic Burden in China Using Autoregressive Integrated Moving Average Model

An Application of Artificial Neural Network for Wind Speeds and Directions Forecasts in Airports

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