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.

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.

Dynamics of the cardiovascular autonomic regulation during orthostatic challenge is more relaxed in women

2018 ◽  
Vol 63 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Sina Reulecke ◽  
Sonia Charleston-Villalobos ◽  
Andreas Voss ◽  
Ramón González-Camarena ◽  
Jesús González-Hermosillo ◽  
...  
Keyword(s):  
Time Series ◽  
Sympathetic Activity ◽  
Low Frequency ◽  
Short Term ◽  
Auto Correlation ◽  
Complete Test ◽  
Auto Correlation Function ◽  
Head Up Tilt ◽  
Cardiovascular Autonomic Regulation ◽  
Sustained Increase

AbstractLinear dynamic analysis of cardiovascular and respiratory time series was performed in healthy subjects with respect to gender by shifted short-term segments throughout a head-up tilt (HUT) test. Beat-to-beat intervals (BBI), systolic (SYS) and diastolic (DIA) blood pressure and respiratory interval (RESP) time series were acquired in 14 men and 15 women. In time domain (TD), the descending slope of the auto-correlation function (ACF) (BBI_a31cor) was more pronounced in women than in men (p<0.05) during the HUT test and considerably steeper (p<0.01) at the end of orthostatic phase (OP). The index SYS_meanNN was slightly but significantly lower (p<0.05) in women during the complete test, while higher respiratory frequency and variability (RESP_sdNN) were found in women (p<0.05), during 10–20 min after tilt-up. In frequency domain (FD), during baseline (BL), BBI-normalized low frequency (BBI_LFN) and BBI_LF/HF were slightly but significantly lower (p<0.05), while normalized high frequency (BBI_HFN) was significantly higher in women. These differences were highly significant from the first 5 min after tilt-up (p<0.01) and highly significant (p<0.001) during 10–14 min of OP. Findings revealed that men showed instantaneously a pronounced and sustained increase in sympathetic activity to compensate orthostatism. In women, sympathetic activity was just increased slightly with delayed onset without considerably affecting sympatho-vagal balance.

Studies on the Price Analysis and Forecasting of Gram Crop in Bhiwani District of Haryana

2020 ◽  
pp. 239-245
Keyword(s):  
Correlation Function ◽  
Lower Limit ◽  
Moving Average ◽  
Arima Model ◽  
Statistical Techniques ◽  
Regulated Markets ◽  
Auto Correlation ◽  
Auto Correlation Function ◽  
Auto Regressive ◽  
Price Analysis

The present study was conducted in Bhiwani district, Siwani and Tosham blocks of Haryana which was selected purposively on the basis of maximum production under gram crop. Further, four regulated markets (Siwani, Dadri, Tosham and Bhiwani) from the Bhiwani district were purposively selected. Average prices in Haryana, data for the period of 2005 to 2016 were analyzed the time series methods. Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) were calculated for the data. Appropriate Box-Jenkins Auto-Regressive Integrated Moving Average (ARIMA) model was fitted. The validity of the model was tested using standard statistical techniques. ARIMA (0, 1, 1) and ARIMA (1, 1, 3) model and used to forecast average prices in Bhiwani for one leading year. The results showed that the average prices forecast for 2017 to be about `4769 per quintal with upper and lower limit `4769 to 4604 per quintal in the Siwani market, `4719 per quintal with upper and lower limit`4719 to 4622 per quintal in Dadri market, `4766 per quintal with upper and lower limit `4766 to4639 per quintal in the Tosham market and `4798 per quintal with upper and lower limit `4798 to 4648 per quintal in the Bhiwani market, respectively.

scholarly journals Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2976 ◽  
Author(s):  
Qinkai Han ◽  
Hao Wu ◽  
Tao Hu ◽  
Fulei Chu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Nonlinear Models ◽  
Moving Average ◽  
Back Propagation ◽  
Percentage Error ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Wind Speed Forecasting

Accurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and hybrid linear/nonlinear models. Original wind speed series is decomposed into a finite number of intrinsic mode functions (IMFs) and residuals by using the EMD. Several popular linear and nonlinear models, including autoregressive integrated moving average (ARIMA), support vector machine (SVM), random forest (RF), artificial neural network with back propagation (BP), extreme learning machines (ELM) and convolutional neural network (CNN), are utilized to study IMFs and residuals, respectively. An ensemble forecast for the original wind speed series is then obtained. Various experiments were conducted on real wind speed series at four wind sites in China. The performance and robustness of various hybrid linear/nonlinear models at two time intervals (10 min and 1 h) are compared comprehensively. It is shown that the EMD based hybrid linear/nonlinear models have better accuracy and more robust performance than the single models with/without EMD. Among the five hybrid models, EMD-ARIMA-RF has the best accuracy on the whole for 10 min data, and the mean absolute percentage error (MAPE) is less than 0.04. However, for the 1 h data, no model can always perform well on the four datasets, and the MAPE is around 0.15.

scholarly journals The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1666 ◽  
Author(s):  
Neeraj Bokde ◽  
Andrés Feijóo ◽  
Nadhir Al-Ansari ◽  
Siyu Tao ◽  
Zaher Mundher Yaseen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Arima Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Power Modeling ◽  
Short Term ◽  
Mode Decomposition ◽  
Eemd Method

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

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