scholarly journals Penerapan Algoritma Kalman Filter dalam Prediksi Kecepatan Angin di Kota Balikpapan

2019 ◽  
Vol 1 (2) ◽  
pp. 25-32
Author(s):  
Irma Fitria ◽  
Primadina Hasanah
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
Daily Activities ◽  
Strong Wind ◽  
Wind Speed Prediction ◽  
Prediction Result ◽  
Pressure Region ◽  
Speed Prediction ◽  
Kalman Filter Algorithm ◽  
Better Than

One of the climate’s elements that has an influence on daily activities is the wind speed. Wind is a movement of air that flows from high pressure to low pressure region. In the shipping and aviation, wind speed is a very important thing to predict. This is due to the wind speed is very influential on the process of the transportation activities. A strong wind can disturb the fluency of transportation. Therefore, information regarding the wind speed prediction is very important to know. In this paper, Kalman Filter algorithm is applied in the wind speed prediction by taking the case in Balikpapan. In this case, the Kalman Filter algorithm is applied to improve the result of ARIMA prediction based on error correction, so we get the prediction result, called ARIMA-Kalman Filter. Based on the simulation result in this study, it can be shown that the prediction result of ARIMA-Kalman Filter is better than ARIMA’s. This is known from the level of accuracy from ARIMA-Kalman Filter, which increased about 65% from ARIMA result.

scholarly journals Machine Intelligent Hybrid Methods Based on Kalman Filter and Wavelet Transform for Short-Term Wind Speed Prediction

Wind ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 37-50
Author(s):  
Yug Patel ◽  
Dipankar Deb
Keyword(s):  
Kalman Filter ◽  
Wavelet Transform ◽  
Wind Speed ◽  
Hybrid Methods ◽  
Moving Average ◽  
Offshore Wind ◽  
Electricity Grid ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Hybrid Forecasting

Wind power’s increasing penetration into the electricity grid poses several challenges for power system operators, primarily due to variability and unpredictability. Highly accurate wind predictions are needed to address this concern. Therefore, the performance of hybrid forecasting approaches combining autoregressive integrated moving average (ARIMA), machine learning models (SVR, RF), wavelet transform (WT), and Kalman filter (KF) techniques is essential to examine. Comparing the proposed hybrid methods with available state-of-the-art algorithms shows that the proposed approach provides more accurate prediction results. The best model is a hybrid of KF-WT-ML with an average R2 score of 0.99967 and RMSE of 0.03874, followed by ARIMA-WT-ML with an average R2 of 0.99796 and RMSE of 0.05863 over different datasets. Moreover, the KF-WT-ML model evaluated on different terrains, including offshore and hilly regions, reveals that the proposed KF based hybrid provides accurate wind speed forecasts for both onshore and offshore wind data.

scholarly journals Accuracy of wind speed predictability with heights using Recurrent Neural networks

2021 ◽  
Vol 49 (4) ◽  
pp. 908-918
Author(s):  
M. Mohandes ◽  
S. Rehman ◽  
H. Nuha ◽  
M.S. Islam ◽  
F.H. Schulze
Keyword(s):  
Wind Speed ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Wind Power Integration ◽  
The Future ◽  
Speed Prediction ◽  
The Mean ◽  
Height Increase ◽  
Absolute Percent Error ◽  
Better Than

Accurate prediction of wind speed in future time domain is critical for wind power integration into the grid. Wind speed is usually measured at lower heights while the hub heights of modern wind turbines are much higher in the range of 80-120m. This study attempts to better understand the predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 20m, 40m, 50m, 60m, 80m, 100m, 120m, 140m, 160m, and 180m heights. This hourly averaged data is used for training and testing a Recurrent Neural Network (RNN) for the prediction of wind speed for each of the future 12 hours, using 48 previous values. Detailed analyses of short-term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights.For example, the mean absolute percent error decreases from 0.19 to 0.16as the height increase from 20m to 180m, respectively for the 12 th future hour prediction. The performance of the proposed method is compared with Multilayer Perceptron (MLP) method. Results show that RNN performed better than MLP for most of the cases presented here at the future 6th hour.

scholarly journals Predicting Daily Mean Wind Speed in Europe Weeks ahead from MJO Status

2020 ◽  
Vol 148 (8) ◽  
pp. 3413-3426
Author(s):  
Llorenç Lledó ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Strong Wind ◽  
Atmospheric Variability ◽  
Wind Speed Prediction ◽  
Mean Wind Speed ◽  
Speed Prediction ◽  
Forecasting System ◽  
Prediction Systems

ABSTRACT The Madden–Julian oscillation (MJO), a prominent feature of the tropical atmospheric circulation at subseasonal time scales, is known to modulate atmospheric variability in the Euro-Atlantic region. However, current subseasonal prediction systems fail to accurately reproduce the physical processes involved in these teleconnection mechanisms. This paper explores the observed impact of strong MJO events on surface wind speed over Europe. It is found that some MJO phases are accompanied by strong wind anomalies in Europe. After showing that this teleconnective mechanism is not present in the predictions of the ECMWF monthly forecasting system, a methodology to reconstruct forecasts of daily mean wind speed in the continent weeks ahead is proposed. This method combines MJO forecasts from the S2S project database and the observed teleconnection impacts in the historical records. Although it is found that strong MJO events cannot be skillfully predicted more than 10 days ahead with current prediction systems, a theoretical experiment shows that this method can effectively transform a dynamical MJO forecast into a probabilistic wind speed prediction in Europe.

THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR

2020 ◽  
Vol 2020 (57) ◽  
pp. 35-41
Author(s):  
K.S. Klen ◽  
◽  
M.K. Yaremenko ◽  
V.Ya. Zhuykov ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Error ◽  
Average Energy ◽  
Interpolation Formula ◽  
Wind Speed Prediction ◽  
Wind Generator ◽  
Prediction Horizon ◽  
Speed Prediction ◽  
Using Data ◽  
Predictor Corrector

The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.

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