NARX Based Short Term Wind Power Forecasting Model

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.

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

Processes ◽

10.3390/pr7110843 ◽

2019 ◽

Vol 7 (11) ◽

pp. 843 ◽

Cited By ~ 5

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.

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Short-Term Wind Power Forecasting Model based on ICA-BP Neural Network

Computer and Information Science ◽

10.5539/cis.v8n1p1 ◽

2014 ◽

Vol 8 (1) ◽

Author(s):

Zi-Cheng Lan ◽

Yuan-Biao Zhang ◽

Jing Zhang ◽

Xin-Guang Lv

Keyword(s):

Neural Network ◽

Wind Power ◽

Bp Neural Network ◽

Forecasting Model ◽

Short Term ◽

Wind Power Forecasting ◽

Model Based ◽

Power Forecasting

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Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Advances in Meteorology ◽

10.1155/2016/8760780 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Haixiang Zang ◽

Lei Fan ◽

Mian Guo ◽

Zhinong Wei ◽

Guoqiang Sun ◽

...

Keyword(s):

Wind Power ◽

Relevance Vector Machine ◽

Ensemble Empirical Mode Decomposition ◽

Power Grids ◽

Forecasting Model ◽

Short Term ◽

Wind Power Forecasting ◽

Forecasting Methods ◽

Forecasting Errors ◽

Power Forecasting

Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD), runs test (RT), and relevance vector machine (RVM). First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF) components and residual (RES) component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels) by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.

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