scholarly journals “Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Dunnan Liu ◽  
Yu Hu ◽  
Yujie Xu ◽  
Canbing Li
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Correction Method ◽  
Relational Model ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Wind Speed Forecasting ◽  
The Stability ◽  
Cloud Theory ◽  
Power Forecasting

As an intermittent energy, wind power has the characteristics of randomness and uncontrollability. It is of great significance to improve the accuracy of wind power forecasting. Currently, most models for wind power forecasting are based on wind speed forecasting. However, it is stuck in a dilemma called “garbage in, garbage out,” which means it is difficult to improve the forecasting accuracy without improving the accuracy of input data such as the wind speed. In this paper, a new model based on cloud theory is proposed. It establishes a more accurate relational model between the wind power and wind speed, which has lots of catastrophe points. Then, combined with the trend during adjacent time and the laws of historical data, the forecasting value will be corrected by the theory of “section to point” correction. It significantly improves the stability of forecasting accuracy and reduces significant forecasting errors at some particular points. At last, by analyzing the data of generation power and historical wind speed in Inner Mongolia, China, it is proved that the proposed method can effectively improve the accuracy of wind speed forecasting.

scholarly journals Distributed Demand Side Management with Stochastic Wind Power Forecasting

2020 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Sergio Grammatico ◽  
Raffaele Carli ◽  
Mariagrazia Dotoli
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Smart Grids ◽  
Demand Side Management ◽  
Expected Value ◽  
Noncooperative Game ◽  
Theory Approach ◽  
Demand Side ◽  
Wind Power Forecasting ◽  
Power Forecasting

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>

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.

scholarly journals Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 843 ◽  
Author(s):  
Keke Wang ◽  
Dongxiao Niu ◽  
Lijie Sun ◽  
Hao Zhen ◽  
Jian Liu ◽  
...  
Keyword(s):  
Wind Power ◽  
Extreme Learning Machine ◽  
Harmony Search ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Correlation Degree ◽  
Learning Machine ◽  
Power Forecasting

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

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

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

Study on a Service Mechanism that Wind/Photovoltaic/Energy Storage Power Station Help Wind Farms Tracking Generation Schedule Curve

2013 ◽  
Vol 860-863 ◽  
pp. 1909-1913
Author(s):  
Hai Xiang Xu ◽  
Peng Wang ◽  
Xiao Meng Ren
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Station ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Assessment Standard ◽  
The Impact ◽  
Power Forecasting

At present, the technology of wind power forecasting isn‘t mature enough in china, so some grid-connected wind farms will be assessed when theirs power forecasting accuracy cant reach the assessment standard. In response to the situation, combined with the characteristics of WPSPS and wind farms, this paper designs a service mechanism that WPSPS help wind farms tracking generation schedule curve, namely, encouraging WPSPS to supply output compensation service for wind farm by market means to increase the accuracy of wind power forecasting. By this mechanism, not only WPSPS and wind farms will achieve win-win, but also the impact on the grid caused by fluctuations of wind powers output will reduce.

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