scholarly journals Research on Short-Term Wind Farm Output Power Prediction Model Based on Meteorological Data Collected by WSN

2013 ◽  
Vol 6 (5) ◽  
pp. 219-226
Author(s):  
Li Ma ◽  
Bo Li ◽  
Du Jie ◽  
Jian Shen ◽  
Jin Wang
Keyword(s):  
Prediction Model ◽  
Output Power ◽  
Wind Farm ◽  
Meteorological Data ◽  
Power Prediction ◽  
Short Term ◽  
Model Based

scholarly journals A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

2020 ◽  
Vol 15 (4) ◽  
Author(s):  
Yongsheng Wang ◽  
Jing Gao ◽  
Zhiwei Xu ◽  
Jidong Luo ◽  
Leixiao Li
Keyword(s):  
Wind Energy ◽  
Prediction Model ◽  
Wind Power ◽  
Output Power ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term Memory ◽  
Evaluation Metrics ◽  
Power Prediction ◽  
Short Term

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.

scholarly journals A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 423 ◽  
Author(s):  
Jidong Wang ◽  
Ran Ran ◽  
Yue Zhou
Keyword(s):  
Prediction Model ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Photovoltaic Power

scholarly journals Short-term Wind Power Prediction Model Based on Encoder-Decoder LSTM

2018 ◽  
Vol 186 ◽  
pp. 012020
Author(s):  
Kuan Lu ◽  
Wen Xue Sun ◽  
Xin Wang ◽  
Xiang Rong Meng ◽  
Yong Zhai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Wind Power Prediction

scholarly journals Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

2019 ◽  
Vol 11 (3) ◽  
pp. 650 ◽  
Author(s):  
Jianguo Zhou ◽  
Xiaolei Xu ◽  
Xuejing Huo ◽  
Yushuo Li
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Meteorological Data ◽  
Frequency Component ◽  
High Frequency Component ◽  
Power Prediction ◽  
Short Term ◽  
Combined Model ◽  
Wind Power Prediction

The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.

scholarly journals Ultra-Short-Term Wind Power Prediction Based on Bidirectional Gated Recurrent Unit and Transfer Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenjin Chen ◽  
Weiwen Qi ◽  
Yu Li ◽  
Jun Zhang ◽  
Feng Zhu ◽  
...  
Keyword(s):  
Wind Power ◽  
Transfer Learning ◽  
Wind Farm ◽  
Meteorological Data ◽  
Prediction Method ◽  
Wind Farms ◽  
Power Prediction ◽  
Short Term ◽  
Training Time ◽  
Gated Recurrent Unit

Wind power forecasting (WPF) is imperative to the control and dispatch of the power grid. Firstly, an ultra-short-term prediction method based on multilayer bidirectional gated recurrent unit (Bi-GRU) and fully connected (FC) layer is proposed. The layers of Bi-GRU extract the temporal feature information of wind power and meteorological data, and the FC layer predicts wind power by changing dimensions to match the output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish the prediction model of a target wind farm with fewer data and less training time based on the source wind farm. The proposed method is validated on two wind farms located in China and the results prove its superior prediction performance compared with other approaches.

scholarly journals Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2857 ◽  
Author(s):  
Yufei Wang ◽  
Li Zhu ◽  
Hua Xue
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Prediction Models ◽  
Short Term Memory ◽  
Single Step ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Emotional Neural Network

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.

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