Wind speed prediction based on spatio-temporal covariance model using Autoregressive Integrated Moving Average regression smoothing

2021 ◽  
Author(s):  
Yu Wang ◽  
Changan Zhu ◽  
Xiaodong Ye ◽  
Jianghai Zhao ◽  
Deji Wang
Keyword(s):  
Wind Speed ◽  
Moving Average ◽  
Covariance Model ◽  
Autoregressive Integrated Moving Average ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Spatio Temporal

scholarly journals A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6782
Author(s):  
Meftah Elsaraiti ◽  
Adel Merabet
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wind Speed ◽  
Short Term Memory ◽  
Moving Average ◽  
Clean Energy ◽  
Autoregressive Integrated Moving Average ◽  
Wind Speed Prediction ◽  
Speed Prediction

Forecasting wind speed has become one of the most attractive topics to researchers in the field of renewable energy due to its use in generating clean energy, and the capacity for integrating it into the electric grid. There are several methods and models for time series forecasting at the present time. Advancements in deep learning methods characterize the possibility of establishing a more developed multistep prediction model than shallow neural networks (SNNs). However, the accuracy and adequacy of long-term wind speed prediction is not yet well resolved. This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), which is a special type of RNN model, compared to the common autoregressive integrated moving average (ARIMA). The results are measured by the root mean square error (RMSE) method. The comparison result shows that the LSTM method is more accurate than ARIMA.

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 Prediksi Cuaca Kota Surabaya Menggunakan Autoregressive Integrated Moving Average (Arima) Box Jenkins dan Kalman Filter

2018 ◽  
Vol 4 (1) ◽  
pp. 59-67
Author(s):  
Nurissaidah Ulinnuha ◽  
Yuniar Farida
Keyword(s):  
Kalman Filter ◽  
Moving Average ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Autoregressive Integrated Moving Average ◽  
Wind Speed Prediction ◽  
The People ◽  
Speed Prediction ◽  
The Impact

Season changes conditions in Indonesia cause many disasters such as landslides, floods and whirlwinds and even hail. Extreme weather conditions that occur, it is better to remain alert to anticipate the various possibilities that occur and to reduce and minimize the impact that can harm the people. The design of weather prediction system in this research using Autoregressive Integrated Moving Average ARIMA Box Jenkins model and Kalman filter with the aim to predict the increasingly extreme weather of Surabaya city at the end of 2017. In this research, weather prediction focused on humidity, temperature, and velocity wind with results 5 days later. The prediction of Surabaya city weather using ARIMA method - Kalman filter obtained the smallest error goal (error MAPE) of 0.000014 each for the prediction of humidity, 0.000037 for temperature prediction, and 0.0123 for wind speed prediction.

Spatio-temporal Wind Speed Prediction of Multiple Wind Farms Using Capsule Network

2021 ◽  
Author(s):  
Ling Zheng ◽  
Bin Zhou ◽  
Siu Wing Or ◽  
Yijia Cao ◽  
Huaizhi Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Spatio Temporal

scholarly journals Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 705 ◽  
Author(s):  
Qiaomu Zhu ◽  
Jinfu Chen ◽  
Lin Zhu ◽  
Xianzhong Duan ◽  
Yilu Liu
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Temporal Correlation ◽  
Learning Approach ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Spatio Temporal

Wind speed prediction using spatio-temporal covariance

2014 ◽  
Vol 75 (2) ◽  
pp. 1435-1449 ◽  
Author(s):  
Anup Suryawanshi ◽  
Debraj Ghosh
Keyword(s):  
Wind Speed ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Spatio Temporal

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.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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