Wind Power Forecasting Model Fusion Evaluation Based on Comprehensive Weights

2016 ◽  
Author(s):  
Jianyan Tian ◽  
Tingting Liu ◽  
Amit Banerjee ◽  
Aixue Wei ◽  
Shengqiang Yang ◽  
...  
Keyword(s):  
Wind Power ◽  
Evaluation Method ◽  
Forecasting Model ◽  
Model Optimization ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Single Index ◽  
Forecasting Models ◽  
The Individual ◽  
Power Forecasting

Studies show that fusion modeling can improve the forecasting accuracy of wind power. Fusion modeling is the process of selective use of information from individual forecasting models. The reasonable evaluation of the individual models is the premise and basis of model optimization so that the individual models with high forecasting accuracy can be selected to establish the fusion model. Because the results of a single index model evaluation may not be comprehensive, the multi-index fusion evaluation method based on maximizing deviations and subjective correction is proposed. The method is applied to the selection of short-term wind power forecasting models. Firstly, this method establishes the individual model base of wind power forecasting model. Secondly, it establishes the more comprehensive evaluation index system. Thirdly, it combines maximizing deviations with the subjective correction coefficient to determine the comprehensive weight of each model, which is used to calculate the fusion evaluation value and get the evaluation order to achieve the model optimization. Finally, based on five years of data from a wind power plant in Shanxi Province, the validated experiments by multiple sets of forecasting data have been done using MATLAB in this paper. The simulation results demonstrate that the evaluation based on the proposed fusion evaluation method is more comprehensive and stable compared to evaluation using a single index. More importantly, it can effectively guide the model optimization with simple operating steps.

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.

An improved wind power forecasting model based on eliminating outliers

2016 ◽  
Vol 40 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Jingxin Guo ◽  
Xiao-Yu Zhang ◽  
Wenling Jang ◽  
Hongqing Wang
Keyword(s):  
Wind Power ◽  
Forecasting Model ◽  
Wind Power Forecasting ◽  
Model Based ◽  
Power Forecasting

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.

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.

scholarly journals Forecasting renewable energy for environmental resilience through computational intelligence

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256381
Author(s):  
Mansoor Khan ◽  
Essam A. Al-Ammar ◽  
Muhammad Rashid Naeem ◽  
Wonsuk Ko ◽  
Hyeong-Jin Choi ◽  
...  
Keyword(s):  
Wind Power ◽  
Wind Turbines ◽  
Energy Transition ◽  
Absolute Error ◽  
Offshore Wind ◽  
Design Environment ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Offshore Wind Turbines ◽  
Power Forecasting

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

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