A novel reinforced online model selection using Q-learning technique for wind speed prediction

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.

Download Full-text

PSO-NN-Based Hybrid Model for Long-Term Wind Speed Prediction: A Study on 67 Cities of India

Advances in Intelligent Systems and Computing - Applications of Artificial Intelligence Techniques in Engineering ◽

10.1007/978-981-13-1822-1_29 ◽

2018 ◽

pp. 319-327 ◽

Cited By ~ 20

Author(s):

Hasmat Malik ◽

Vinoop Padmanabhan ◽

R. Sharma

Keyword(s):

Wind Speed ◽

Hybrid Model ◽

Wind Speed Prediction ◽

Speed Prediction

Download Full-text

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

Wind Engineering ◽

10.1177/0309524x19849843 ◽

2019 ◽

Vol 44 (3) ◽

pp. 266-281 ◽

Cited By ~ 1

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.

Download Full-text

Applied machine learning in wind speed prediction and loss minimization in unbalanced radial distribution system

Energy Sources Part A Recovery Utilization and Environmental Effects ◽

10.1080/15567036.2020.1859010 ◽

2020 ◽

pp. 1-21

Author(s):

Aliva Routray ◽

Khyati D Mistry ◽

Sabha Raj Arya ◽

B. Chittibabu

Keyword(s):

Machine Learning ◽

Wind Speed ◽

Radial Distribution ◽

Distribution System ◽

Loss Minimization ◽

Radial Distribution System ◽

Wind Speed Prediction ◽

Applied Machine Learning ◽

Speed Prediction

Download Full-text

Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory

Neural Computing and Applications ◽

10.1007/s00521-021-06016-4 ◽

2021 ◽

Author(s):

Farah Shahid ◽

Aneela Zameer ◽

Muhammad Javaid Iqbal

Keyword(s):

Wind Speed ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Wind Speed Prediction ◽

Speed Prediction ◽

Long Short Term Memory

Download Full-text

A Novel Information Theoretical Criterion for Climate Network Construction

Symmetry ◽

10.3390/sym12091500 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1500

Author(s):

Sara Cornejo-Bueno ◽

Mihaela I. Chidean ◽

Antonio J. Caamaño ◽

Luis Prieto-Godino ◽

Sancho Salcedo-Sanz

Keyword(s):

Wind Speed ◽

Wind Farms ◽

Construction Method ◽

Membership Probability ◽

Network Construction ◽

Wind Speed Prediction ◽

Emergent Behaviour ◽

Symmetry Properties ◽

Leibler Divergence ◽

Speed Prediction

This paper presents a novel methodology for Climate Network (CN) construction based on the Kullback-Leibler divergence (KLD) among Membership Probability (MP) distributions, obtained from the Second Order Data-Coupled Clustering (SODCC) algorithm. The proposed method is able to obtain CNs with emergent behaviour adapted to the variables being analyzed, and with a low number of spurious or missing links. We evaluate the proposed method in a problem of CN construction to assess differences in wind speed prediction at different wind farms in Spain. The considered problem presents strong local and mesoscale relationships, but low synoptic scale relationships, which have a direct influence in the CN obtained. We carry out a comparison of the proposed approach with a classical correlation-based CN construction method. We show that the proposed approach based on the SODCC algorithm and the KLD constructs CNs with an emergent behaviour according to underlying wind speed prediction data physics, unlike the correlation-based method that produces spurious and missing links. Furthermore, it is shown that the climate network construction method facilitates the evaluation of symmetry properties in the resulting complex networks.

Download Full-text

A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

IEEE Access ◽

10.1109/access.2020.3041533 ◽

2020 ◽

Vol 8 ◽

pp. 215892-215903

Author(s):

Ji Jin ◽

Bin Wang ◽

Min Yu ◽

Jiang Liu ◽

Wenbo Wang

Keyword(s):

Wind Speed ◽

Prediction Model ◽

Wind Speed Prediction ◽

Speed Prediction ◽

Atmospheric Motion ◽

Self Adaptive

Download Full-text

Wind Speed Forecasting Using a Combined Method Based on Auto Regression and Wavelet Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.512-515.803 ◽

2012 ◽

Vol 512-515 ◽

pp. 803-808

Author(s):

Ji Long Tong ◽

Zeng Bao Zhao ◽

Wen Yu Zhang

Keyword(s):

Wind Speed ◽

Original Data ◽

Ar Model ◽

Wind Speed Forecasting ◽

Wind Speed Prediction ◽

Prediction Time ◽

New Strategy ◽

Speed Prediction ◽

Auto Regression

This paper presents a new strategy in wind speed prediction based on AR model and wavelet transform.The model uses the adjacent data for short-term wind speed forecasting and the data of the same moment in earlier days for long-term wind speed prediction at that moment,taking the similarity of wind speed at the same moment every day into account.Using the new model to analyze the wind speed of An-xi,China in April,2010,this paper concludes that the model is effective for that the correlation coefficient between the predicted value and the original data is larger than 0.8 when the prediction is less than 48 hours;while the prediction time is long ahead (48-120h),the error is acceptable (within 40%),which demonstrates that the new method is a novel and good idea for prediction on wind speed.

Download Full-text

A multiobjective framework for wind speed prediction interval forecasts

Renewable Energy ◽

10.1016/j.renene.2015.08.038 ◽

2016 ◽

Vol 87 ◽

pp. 903-910 ◽

Cited By ~ 35

Author(s):

Nitin Anand Shrivastava ◽

Kunal Lohia ◽

Bijaya Ketan Panigrahi

Keyword(s):

Wind Speed ◽

Prediction Interval ◽

Wind Speed Prediction ◽

Speed Prediction

Download Full-text