Time-series models for reliability evaluation of power systems including wind energy

1996 ◽  
Vol 36 (9) ◽  
pp. 1253-1261 ◽  
Author(s):  
R. Billinton ◽  
Hua Chen ◽  
R. Ghajar
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Wind Energy ◽  
Reliability Evaluation ◽  
Time Series Models

scholarly journals State of the art in wind speed in England using BATS ,TBATS , Holt’s Linear and ARIMA model

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 129-138
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh ◽  
Aynur Yonar ◽  
Amr Badr ◽  
Pradeep Mishra ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Energy ◽  
Linear Trend ◽  
Renewable Energy Sources ◽  
Arima Model ◽  
Training Data ◽  
Time Series Models ◽  
Testing Data ◽  
Mean Square Errors

Wind energy is one of the most important renewable energy sources in the world. Hence, the prediction of wind speed is a highly significant subject with respect to both protecting the environment and economic development. England is among the countries with an increasing interest in the potential for wind energy systems. In this study, various time series models, including BATS, TBATS, Holt’s Linear Trend, and ARIMA models were applied for wind speed prediction in England, and their performance was compared. The available wind speed data between 1994-07-07 and 2015-12-31 were divided into two parts: training data that is used to build up the models and testing data that is used to measure the validity of a model forecast. The results of the testing data indicate that the BATS and ARIMA outperform the other time series models according to the root mean square errors.

Multivariate time series models for studies on stochastic generators in power systems

2010 ◽  
Vol 80 (3) ◽  
pp. 265-276 ◽  
Author(s):  
Bernd Klöckl ◽  
George Papaefthymiou
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Multivariate Time Series ◽  
Time Series Models

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Recent Developments

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Modeling and Forecasting Africas GDP with Time Series Models

2018 ◽  
Vol 8 (4) ◽  
Author(s):  
Annie Uwimana ◽  
B. I. Xiuchun ◽  
ZHANG Shuguang
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Modeling And Forecasting

A Graphical Similarity Measure for Time Series Models.

1985 ◽  
Author(s):  
B. Kedem
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Time Series Models

Time Series Models for Taiwan's CPI

2015 ◽  
Author(s):  
Lawrence Hsiao
Keyword(s):  
Time Series ◽  
Time Series Models
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