Impact of the accuracy of NWP wind speed forecasts on wind power forecasting

2019 ◽  
Author(s):  
Yimei Wang ◽  
Yongqian Liu ◽  
Peng Song ◽  
Hui Liu ◽  
Zechun Hu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Power 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.

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
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