Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
The design, operational planning, and integration of wind power plants with other renewables and the grid face challenges attributed to the intermittent nature of wind power generation. Addressing this issue necessitates the development of a smart wind power (and in particular wind speed) forecasting approach. This is a complex task due to substant...
Contexts in source publication
Context 1
... evaluate and compare the models precisely, the RMSE, MAE, and MSLE of each model's results are calculated based on what was explained in the methodology section. The results are reported in Table 4. The LSTM model results show a 2.21-3.16 ...Context 2
... the probability distribution of the wind speed with the LSTM model and using it as an input feature could be one of the alternatives to boost the LSTM ability. The results in Table 4 show that the proposed integrated LSTM-Weibull model can reduce the RMSE of the single LSTM model in forecasting winter, summer, and fall representative days by about 13, 39, and 31 percent, respectively. These error reductions show that adding a proper feature, such as the Weibull probability of the wind speed, can help LSTM accurately forecast the future. ...Context 3
... training the model with different types of historical data and parameters such as learning rate could lead to different results [37], from what was shown in Table 4 and from significant result changes from changing the forecasting horizon, validating the results of this study with the results of the other research could not be insightful. However, based on the literature [38], the mean absolute percentage error for wind forecasting ranged between 25% to 40%. ...Context 4
... based on similar research [4] that forecasted wind speed in four different sites in China, the RMSE of their proposed hybrid model for daily wind forecast ranged between 1.6-1.8. Comparing this result with the output of the hybrid model presented in this research (Table 4) for a two-days-ahead forecast shows acceptable prediction accuracy. ...Context 5
... training the model with different types of historical data and parameters such as learning rate could lead to different results [37], from what was shown in Table 4 and from significant result changes from changing the forecasting horizon, validating the results of this study with the results of the other research could not be insightful. However, based on the literature [38], the mean absolute percentage error for wind forecasting ranged between 25% to 40%. ...Context 6
... based on similar research [4] that forecasted wind speed in four different sites in China, the RMSE of their proposed hybrid model for daily wind forecast ranged between 1.6-1.8. Comparing this result with the output of the hybrid model presented in this research (Table 4) for a two-days-ahead forecast shows acceptable prediction accuracy. ...Citations
... 2) Physical methods first require the implementation of NWP for wind farms, including grid prediction of meteorological factors such as wind speed, humidity, and direction [17], in order to complete wind power prediction for wind farms. The modeling of physical models considers atmospheric motion and geographical environment, and the calculation process reflects the coupling effect of multiple influencing factors. ...
To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve, and historical power is reconstructed using variational mode decomposition (VMD) filtering to form input variables in chronological order. Finally, input variables of a single turbine enter the temporal convolutional network (TCN) to complete initial feature extraction, and then integrate the outputs of all TCN layers using Long Short Term Memory Networks (LSTM) to obtain power prediction sequences for all turbine positions. The proposed method was tested on a wind farm connected to a distributed power grid, and the results showed it to be superior to existing typical methods.
... Tree-based models encompass decision tree (DT), RF [86], gradient boosting decision tree (GBDT), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost) [87], and light gradient boosting machine (LightGBM) [88]. With the advancement of DL technologies, deep neural networks (DNNs), including RNN [89], LSTM [90], BiLSTM [91], GRU [92], BiGRU, DBN, deep ELM (DELM), and Transformer have been widely applied in WSP and WPP due to their outstanding capability in handling complex nonlinear problems. Ding et al. [90] used CEEMD to decompose the non-stationary wind power time series into a series of relatively stationary components. ...
Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.
