Statistical Time Series Models for Wind Speed Forecasting

2020 ◽  
pp. 233-243
Author(s):  
Anil Kumar Kushwah ◽  
Rajesh Wadhvani ◽  
Varsha Kushwah
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Models ◽  
Wind Speed Forecasting ◽  
Statistical Time

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

scholarly journals Improving Inflation Forecasts in the Medium to Long Term

2013 ◽  
pp. 1-6 ◽  
Author(s):  
Saeed Zaman
Keyword(s):  
Time Series ◽  
Forecast Accuracy ◽  
Time Series Models ◽  
Powerful Technique ◽  
Inflation Forecasts ◽  
Statistical Time

A simple but powerful technique for incorporating a changing underlying inflation trend into standard statistical time series models can improve forecast accuracy significantly—about 20 percent to 30 percent, two to three years out.

scholarly journals Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

Atmosphere ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 77 ◽  
Author(s):  
Henrique do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
João Verçosa Leal Junior ◽  
Daniel von Glehn dos Santos ◽  
Paulo Cesar Marques de Carvalho
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Hybrid Modeling ◽  
Time Series Models ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Artificial Neural

Short-Term Wind Speed Forecasting Based on Time Series-State Transition Method

2013 ◽  
Vol 291-294 ◽  
pp. 2298-2301
Author(s):  
Jie Ji ◽  
Yong Tao Shen ◽  
Meng Si Tan ◽  
Li Ning Wu ◽  
Jian Hua Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
State Transition ◽  
Operating Cost ◽  
Transition Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Forecasting Methods ◽  
High Volatility ◽  
State Transition Model

Wind speed forecasting is of great significance to the improvement of grid stability and the reduction of operating cost. Because of the high volatility of wind speed, the accuracy of current forecasting methods have yet to be improved. This paper established a time series-state transition model to solve this problem, which uses state transition to revise the results of time series forecasting. The improvement in accuracy is proved by the analysis of a practical example.

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