Multistage Model for Short Term Wind Power Forecasting

2011 ◽  
Author(s):  
J. Shi ◽  
Y. Q. Liu ◽  
Y. P. Yang ◽  
S. Han ◽  
W. J. Lee
Keyword(s):  
Wind Power ◽  
Linear Models ◽  
Wind Farms ◽  
Support Vector ◽  
Climatic Data ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Multistage Model ◽  
National Climatic Data Center ◽  
Power Forecasting

The increased integration of wind power into the electric grid poses new challenges due to its fluctuation and volatility. Short term wind power forecasting is one of the most effective ways to deal with it. Various individual non-linear models are proposed to meet the data requirement to forecast short term wind power. However, as every model has its advantage and weakness, when these models are applied to different wind farms, the forecasting accuracy of every model varies because of distinct data character. This paper analyzes individual forecast models like Wavelet Transform and Support Vector Machine (SVM), and then puts forward a complex-valued forecasting model which is based on Artificial Natural Network in accordance with forecasting data provided by National Climatic Data Center in U.S. The existing individual models are matched and trained according to certain means by Natural Network to propose a multistage model. For variable data from different wind farms, the model can adjust and optimize portion of individual models. Compared with each single model, the multistage model has more robust adaptation and faster calculation speed, which can improve the forecasting precision and have more engineering value.

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

Energies ◽  
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.

Short-Term Wind Power Forecasting in Wind Power Plants of North China Based on Support Vector Regression Analysis

2014 ◽  
Vol 705 ◽  
pp. 284-288
Author(s):  
Hai Jian Shao ◽  
Hai Kun Wei
Keyword(s):  
Support Vector Regression ◽  
Wind Power ◽  
Power Plants ◽  
Heuristic Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Wind Power Plants ◽  
Power Forecasting

This paper investigates the short-term wind power forecasting and demonstrates accurate modeling, which utilizes two representative heuristic algorithms (i.e. wavelet neural network (WNN) and Multilayer Perceptron (MLP)), and statistical machine learning techniques (i.e. Support Vector Regression (SVR)). The proposed method generates the performances of different approaches for random time series, characterized with high accuracy and high generalization capability. The employed data is obtained through Sampling equipment in Real Wind Power Plants (Power generation equipment is Dongfang Steam Turbine Co., Ltd. weak wind turbine type--FD77 with German REpower company technology). The main innovation of this paper comes from: (a) problem may encounter in the real application is in consideration such as corrupt, missing value and noisy data. (b) Data lag estimation are provided to investigate the data distribution and obtain the best input variables, respectively. (c) Comparison between MLP neural networks, WNN and SVR with optimized kernel parameters based on Grid-search method are provided to demonstrate the best forecasting approaches. The purpose of this paper is to provide a method with reference value for short-term wind power forecasting.

scholarly journals Robustness of Short-Term Wind Power Forecasting against False Data Injection Attacks

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3780 ◽  
Author(s):  
Yao Zhang ◽  
Fan Lin ◽  
Ke Wang
Keyword(s):  
Wind Power ◽  
Support Vector ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Wind Power Forecasting ◽  
False Data Injection ◽  
Forecasting Models ◽  
Injection Attacks ◽  
The Impact ◽  
Power Forecasting

The accuracy of wind power forecasting depends a great deal on the data quality, which is so susceptible to cybersecurity attacks. In this paper, we study the cybersecurity issue of short-term wind power forecasting. We present one class of data attacks, called false data injection attacks, against wind power deterministic and probabilistic forecasting. We show that any malicious data can be injected to historical data without being discovered by one of the commonly-used anomaly detection techniques. Moreover, we testify that attackers can launch such data attacks even with limited resources. To study the impact of data attacks on the forecasting accuracy, we establish the framework of simulating false data injection attacks using the Monte Carlo method. Then, the robustness of six representative wind power forecasting models is tested. Numerical results on real-world data demonstrate that the support vector machine and k-nearest neighbors combined with kernel density estimator are the most robust deterministic and probabilistic forecasting ones among six representative models, respectively. Nevertheless, none of them can issue accurate forecasts under very strong false data attacks. This presents a serious challenge to the community of wind power forecasting. The challenge is to study robust wind power forecasting models dealing with false data attacks.

scholarly journals Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hong Zhang ◽  
Lixing Chen ◽  
Yong Qu ◽  
Guo Zhao ◽  
Zhenwei Guo
Keyword(s):  
Support Vector Regression ◽  
Wind Power ◽  
Real Data ◽  
Search Method ◽  
Support Vector ◽  
Grid Search ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Grid Search Method ◽  
Power Forecasting

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.

scholarly journals A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting

2019 ◽  
Vol 9 (15) ◽  
pp. 3019 ◽  
Author(s):  
Huan Zheng ◽  
Yanghui Wu
Keyword(s):  
Wind Power ◽  
Gradient Boosting ◽  
Support Vector ◽  
Similarity Analysis ◽  
Feature Engineering ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
Power Forecasting

Large-scale wind power access may cause a series of safety and stability problems. Wind power forecasting (WPF) is beneficial to dispatch in advance. In this paper, a new extreme gradient boosting (XGBoost) model with weather similarity analysis and feature engineering is proposed for short-term wind power forecasting. Based on the similarity among historical days’ weather, k-means clustering algorithm is used to divide the samples into several categories. Additionally, we also create some time features and drop unimportant features through feature engineering. For each category, we make predictions using XGBoost. The results of the proposed model are compared with the back propagation neural network (BPNN) and classification and regression tree (CART), random forests (RF), support vector regression (SVR), and a single XGBoost model. It is shown that the proposed model produces the highest forecasting accuracy among all these models.

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