Short-Term Wind Power Prediction Based on Wavelet Decomposition and Extreme Learning Machine

Wang, Xin; Zheng, Yongjun; Li, Lixue; Zhou, Lei; Yao, Gang; Huang, Ting

doi:10.1007/978-3-642-31362-2_71

Cited by 5 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can predict from the essence of wind energy, thereby improving the prediction accuracy. Common methods include neural network [79], wavelet analysis [44,80], and support vector machine [73,78,79,81]. Hybrid predictive models of artificial intelligence methods are becoming increasingly popular, not only increasing the complexity of algorithms but also enhancing the forecasting of wind power generation.…”

Section: Reviews For Technologies and Applicationsmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

View full text Add to dashboard Cite

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

show abstract

Section: Reviews For Technologies and Applicationsmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…GNN model performed better than the normal ANN. In [135], a novel ANN-based model for the prediction of solar irradiance is proposed. Instead of considering solar irradiance as the only parameter in training ANN, an input vector that consists of statistical feature parameters is constructed.…”

Section: Review Of Machine Learning Techniques For Solar Forecastmentioning

confidence: 99%

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

Prema¹,

Bhaskar

Almakhles

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.

show abstract

“…Deep learning methods use artificial intelligence to describe the nonlinear relationship between input and output. Common methods, such as neural networks [20], wavelet analysis [21], and support vector machines [22,23], improve the accuracy and adaptability of the model by correcting errors.…”

Section: Introductionmentioning

confidence: 99%

“…In optimization algorithms, there is a necessity for new algorithms that can improve the performance of the existing algorithms while enhancing bee swarm optimization to perform the parameter adjusting approach, which has an important ability in improving the performance of the BSO. In [20][21][22][23], it is noted that accurate wind forecasting is crucial to have a reliable power system. However, the intermitted and unstable nature of the wind speed makes it is very difficult to accurately forecast the power generated.…”

Section: Introductionmentioning

confidence: 99%

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

Hong

Huang

et al. 2020

Energies

View full text Add to dashboard Cite

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.

show abstract

Short-Term Wind Power Prediction Based on Wavelet Decomposition and Extreme Learning Machine

Cited by 5 publications

References 9 publications

A Review of Modern Wind Power Generation Forecasting Technologies

A Review of Modern Wind Power Generation Forecasting Technologies

Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast

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

Contact Info

Product

Resources

About