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 A PSO-LSTM Model of Offshore Wind Power Forecast considering the Variation of Wind Speed in Second-Level Time Scale

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chao Yuan ◽  
Yiming Tang ◽  
Rui Mei ◽  
Fei Mo ◽  
Hong Wang
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Time Scale ◽  
Forecast Model ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Wind Speed Prediction ◽  
Wind Power Forecast ◽  
Speed Prediction ◽  
Power Sequence

To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.

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 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.

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