Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review

In this paper, we propose a distributed demand side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach.<br>We assume that each user selfishly formulates its grid optimization problem as a noncooperative game.<br>The core challenge in this paper is defining an approach to cope with the uncertainty in wind power availability. <br>We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework.<br>In the latter case, we employ the sample average approximation technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability.<br><div>Numerical simulations on a real dataset show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Control Systems Technology<br></div>

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Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽

10.3390/en13236319 ◽

2020 ◽

Vol 13 (23) ◽

pp. 6319

Author(s):

Chia-Sheng Tu ◽

Chih-Ming Hong ◽

Hsi-Shan Huang ◽

Chiung-Hsing Chen

Keyword(s):

Support Vector Machine ◽

Wind Speed ◽

Regression Model ◽

Wind Power ◽

Support Vector ◽

Forecasting Model ◽

Short Term ◽

Wind Power Forecasting ◽

Data Regression ◽

Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

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Wind power forecasting based on daily wind speed data using machine learning algorithms

Energy Conversion and Management ◽

10.1016/j.enconman.2019.111823 ◽

2019 ◽

Vol 198 ◽

pp. 111823 ◽

Cited By ~ 34

Author(s):

Halil Demolli ◽

Ahmet Sakir Dokuz ◽

Alper Ecemis ◽

Murat Gokcek

Keyword(s):

Machine Learning ◽

Wind Speed ◽

Wind Power ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Wind Power Forecasting ◽

Wind Speed Data ◽

Power Forecasting

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A review of wind speed and wind power forecasting with deep neural networks

Applied Energy ◽

10.1016/j.apenergy.2021.117766 ◽

2021 ◽

Vol 304 ◽

pp. 117766

Author(s):

Yun Wang ◽

Runmin Zou ◽

Fang Liu ◽

Lingjun Zhang ◽

Qianyi Liu

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Wind Power ◽

Deep Neural Networks ◽

Wind Power Forecasting ◽

Power Forecasting

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Wind power forecasting — An application of machine learning in renewable energy

2014 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2014.6889771 ◽

2014 ◽

Cited By ~ 5

Author(s):

Gul Muhammad Khan ◽

Jawad Ali ◽

Sahibzada Ali Mahmud

Keyword(s):

Machine Learning ◽

Renewable Energy ◽

Wind Power ◽

Wind Power Forecasting ◽

Power Forecasting

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Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review

IET Renewable Power Generation ◽

10.1049/iet-rpg.2015.0477 ◽

2016 ◽

Vol 10 (7) ◽

pp. 885-989 ◽

Cited By ~ 57

Author(s):

Edward Baleke Ssekulima ◽

Mohamed Shawky El Moursi ◽

Amer Al Hinai ◽

Muhammad Bashar Anwar

Keyword(s):

Renewable Energy ◽

Wind Speed ◽

Solar Irradiance ◽

Energy Integration ◽

Renewable Energy Integration ◽

Forecasting Techniques

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Multi-time series and -time scale modeling for wind speed and wind power forecasting part II: Medium-term and long-term applications

2015 International Conference on Renewable Energy Research and Applications (ICRERA) ◽

10.1109/icrera.2015.7418698 ◽

2015 ◽

Cited By ~ 3

Author(s):

Ilhami Colak ◽

Seref Sagiroglu ◽

Mehmet Yesilbudak ◽

Ersan Kabalci ◽

H. Ibrahim Bulbul

Keyword(s):

Time Series ◽

Wind Speed ◽

Wind Power ◽

Time Scale ◽

Medium Term ◽

Wind Power Forecasting ◽

Scale Modeling ◽

Power Forecasting

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Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting

Sustainability ◽

10.3390/su132413609 ◽

2021 ◽

Vol 13 (24) ◽

pp. 13609

Author(s):

Diaa Salman ◽

Mehmet Kusaf

Keyword(s):

Renewable Energy ◽

Power Systems ◽

Wind Power ◽

Unit Commitment ◽

Optimization Technique ◽

Test System ◽

Optimization Techniques ◽

Support Vector ◽

Wind Power Forecasting ◽

Power Forecasting

Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks.

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