Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

Wind speed forecasting is important for wind energy forecasting.In the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed accurately.The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overfitting and non-linearity issues.The curse of dimensionality and overfitting issues are handled by using Boruta feature selec-tion.The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed forecasting.The model identifies relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time steps.The proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and R2.The BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of 0.8766.The experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

Coupling framework for a wind speed forecasting model applied to wind energy

Wind energy is the burgeoning renewable energy. Accurate wind speed prediction is necessary to ensure the stability and reliability of the power grid for wind energy. This study focuses on developing a novel hybrid forecasting model to tackle adverse effects caused by strong variability and abrupt changes in wind speed. The hybrid model combines data decomposition and error correction strategy for a wind speed forecasting model applied to wind energy. First, wavelet packet decomposition is applied to wind speed series to obtain stationary subseries. Next, outlier robust extreme learning machine is implemented to predict subseries. Finally, an error correction strategy coupled with data decomposition is designed to repair preliminary prediction results. In addition, four measured datasets from China and USAwind farms with different time intervals are used to evaluate the performance of the proposed approach. Experimental analysis indicates that the proposed model outperforms the compared models. Results show that(1) the prediction accuracy of the proposed model is remarkably improved compared with other conventional models;(2) the proposed model can reduce the influence of the end effect in the decomposition-based forecasting model;(3) the coupling framework is successful for enhancing performance of hybrid forecasting model.

An Experiment on the Prediction of the Surface Wind Speed in Chongli Based on the WRF Model:Evaluation and Calibration

In this study,the ability of the Weather Research and Forecasting(WRF)model to generate accurate near-surface wind speed forecasts at kilometer-to subkilometer-scale resolution along race tracks(RTs)in Chongli during the wintertime is evaluated.The performance of two postprocessing methods,including the decaying-averaging(DA)and analogy-based(AN)methods,is tested to calibrate the near-surface wind speed forecasts.It is found that great uncertainties exist in the model’s raw forecasts of the near-surface wind speed in Chongli.Improvement of the forecast accuracy due to refinement of the horizontal resolution from kilometer to subkilometer scale is limited and not systematic.The RT sites tend to have large bias and centered root mean square error(CRMSE)values and also exhibit notable underestimation of high-wind speeds,notable overestimation or underestimation of the near-surface wind speed at high altitudes,and notable underestimation during daytime.These problems are not resolved by increasing the horizontal resolution and are even exacerbated,which leads to great challenges in the accurate forecasting of the near-surface wind speed in the competition areas in Chongli.The application of postprocessing methods can greatly improve the forecast accuracy of near-surface wind speed.Both methods used in this study have comparable abilities in reducing the(positive or negative)bias,while the AN method is also capable of decreasing the random error reflected by CRMSE.In particular,the large biases for high-wind speeds,wind speeds at high-altitude stations,and wind speeds during the daytime at RT stations can be evidently reduced.

Forecasting wind speed using a reinforcement learning hybrid ensemble model:a high-speed railways strong wind signal prediction study in Xinjiang,China

Considering the application of wind-forecasting technology along the railway,it becomes an effective means to reduce the risk of tain more reliable wind-speed prediction results,this study proposes an intelligent ensemble forecasting method for strong winds train derailment and overturning.Accurate prediction of crosswinds can provide scientific guidance for safe train operation.To obalong the high-speed railway.The method consists of three parts:the data preprocessing module,the hybrid prediction module and original wind speed data.Then,Broyden-Fletcher-Goldfarb-Shanno(BFGS)method,non-linear autoregressive network with exoge-the reinforcement learing ensemble module.First,fast ensemble empirical model decomposition(FEEMD)is used to process the prediction models for all the sublayers of decomposition.Finally,Q-learning is utilized to iteratively calculate the combined weights nous inputs(NARX)and deep belief network(DBN),three benchmark predictors with different characteristics are employed to build of the three models,and the prediction results of each sublayer are superimposed to obtain the model output.The real wind speed data of two railway stations in Xinjiang are used for experimental comparison.Experiments show that compared with the single benchmark model,the hybrid ensemble model has better accumacy and robustness for wind speed prediction along the railway.The 1-step forecasting results mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)of Q-leaming-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s,0.6509%,0.1146 m/s,and 0.0458 m/s.0.2709%,0.0616 m/s.respectively.The proposed ensemble model is a promising method for railway wind speed prediction.

Ultra-short Term Wind Speed Prediction Using Mathematical Morphology Decomposition and Long Short-term Memory

This paper proposes a new model,which consists of a mathematical morphology(MM)decomposer and two long short term memory(LSTM)networks,to perform ultra-short term wind speed forecast.The MM decomposer is developed in order to improve the forecast accuracy,which separates the wind speed into two parts:a stationary long-term baseline and a nonstationary short-term residue.Afterwards,two LSTM networks are implemented to forecast the baseline and residue,respectively.Besides,this paper makes an integrated forecast that takes into account multiple climate factors,such as temperature and air pressure.The baseline,temperature and air pressure are used as the inputs of baseline network for training and prediction,and the baseline,residue,temperature and air pressure are used as the inputs of residue network for training and prediction.The performance of the proposed model has been validated using data collected from the Australian Meteorological Station,which is compared with least squares-support vector machine(LS-SVM),back-propagation artificial neural network(BPNN),LSTM,MM-LS-SVM,and MM-BPNN.The results demonstrate that the proposed model is more suitable to solve non-stationary time-series forecast,and achieves higher accuracy than the other models under various conditions.