An improved combination approach based on Adaboost algorithm for wind speed time series forecasting

2018 ◽  
Vol 160 ◽  
pp. 273-288 ◽  
Author(s):  
Ling Xiao ◽  
Yunxuan Dong ◽  
Yao Dong
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Forecasting ◽  
Adaboost Algorithm ◽  
Combination Approach

scholarly journals Wavelet Transform and Autoregressive Integrated Moving Average Combination Approach in Crude Oil Prices Forecasting

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Nurull Qurraisha Nadiyya Md-Khair ◽  
Ruhaidah Samsudin ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Crude Oil ◽  
Moving Average ◽  
Absolute Error ◽  
Time Series Forecasting ◽  
Autoregressive Integrated Moving Average ◽  
Spot Prices ◽  
Crude Oil Prices ◽  
Combination Approach

This paper proposes a time series forecasting approach combining wavelet transform and autoregressive integrated moving average (ARIMA) to enhance the precision in forecasting crude oil spot prices series. Wavelet transform splits the original prices series into several subseries, then the most appropriate model of ARIMA is established to predict each respective series and finally all series are combined back to get the original series. The datasets for the experiment consist of crude oil spot prices from Brent North Sea (Brent) and West Texas Intermediate (WTI). Single forecasting model ARIMA and several existing forecasting approaches in the literatures are used to measure the performance of the proposed approach by utilizing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) collected. Final results have depicted that the proposed approach outperforms other approaches with smaller MAE and RMSE values. Ultimately, it is proven that data decomposition, combined with forecasting method can increase the accuracy in time series forecasting.

scholarly journals ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2578 ◽  
Author(s):  
Neeraj Dhanraj Bokde ◽  
Zaher Mundher Yaseen ◽  
Gorm Bruun Andersen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Solar Radiation ◽  
Test Bench ◽  
Performance Metrics ◽  
R Package ◽  
Time Series Forecasting ◽  
Comparative Performance ◽  
Forecasting Methods ◽  
Natural Time

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

scholarly journals Wind speed time series forecasting method in wind farm based on EEMD

2021 ◽  
Vol 791 (1) ◽  
pp. 012140
Author(s):  
Yu Dongyang ◽  
Sun Fengchang ◽  
Deng Xiaochuan ◽  
Wang Zheng ◽  
Wu Jiahua ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Farm ◽  
Time Series Forecasting ◽  
Forecasting Method

scholarly journals Application of Facebook’s Prophet Model for Forecasting Meteorological Data

2021 ◽  
Vol 21 (2) ◽  
pp. 53-58
Author(s):  
Junsuk Kim ◽  
Tae Jin Kim
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Meteorological Data ◽  
Risk Index ◽  
Basic Research ◽  
Time Series Forecasting ◽  
Spreading Speed ◽  
Future Research ◽  
Fire Occurrence ◽  
Computing Speed

The wildfire risk index was calculated based on current meteorological information, for example, temperature, humidity, and wind speed. Thus, meteorological data forecasting could help estimate the probability of fire occurrence or spreading speed to prevent large wildfires. This study predicts meteorological data (e.g., temperature, humidity, and wind speed) using Facebook's Prophet library. We trained the Prophet model using meteorological data between 2016 and 2018 in Goseong, Gangwon-do (where the wildfire occurred in 2019) and predicted meteorological data for the first four months in 2019. We obtained that Facebook's Prophet model was effective in computing speed and predicting the overall trend. However, it could not predict sudden irregular changes satisfactorily. Considering its rapidity, these results could play an important role in future research, especially as a basic research for time-series forecasting.

Wind Speed Time Series Forecasting Using a Neural Network Model Inspired Biologically

2018 ◽  
Author(s):  
Sandra M. Valdivia Bautista ◽  
Rangel-Carrillo Eduardo ◽  
Marco A. Perez Cisneros ◽  
Luis J. Ricalde ◽  
Miguel A. Olmos Gomez ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Forecasting

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

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