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Deep Learning for wind speed forecasting Using Bi-LSTM with Selected Features

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Intelligent Automation & Soft Computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** 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 *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** 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 *** 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 *** 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 *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

On Comparing Six Optimization Algorithms for Network-based wind speed forecasting

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On Comparing Six Optimization Algorithms for Network-based W...

Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for forecasting Solar Irradiation and wind speed

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作者： Huanjie Tao Xiaobo Lu School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

Network-based wind speed forecasting has played an important role in the power system. The network parameters optimization is an important issue, and different optimization algorithms are believed to result in different forecasting accuracies. In this paper, six network parameters optimization algorithms, including Gradient descent, Momentum, Ada Grad, RMSprop, Adam, and Adadelta, are implemented and compared in the application of wind speed forecasting. As a case study, this paper uses a wind speed data obtained from Ningxia, China. The performance is evaluated by three metrics, namely, mean absolute error（MAE）, root mean square error（RMSE）, and mean absolute percentage error（MAPE）. The experiment results show that, Adam algorithm and RMSprop algorithm achieve better forecasting accuracy and less training time than the other optimization algorithms. This study can be a guide to the selection of optimization algorithms on wind speed forecasting problems for researchers.

关键词： wind speed forecasting Back-propagation neural network forecasting accuracy Optimization algorithms

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Computers, Materials & Continua 2023年第6期75卷 6375-6393页

作者： Neelam Mughees Mujtaba Hussain Jaffery Abdullah Mughees Anam Mughees Krzysztof Ejsmont Department of Electrical and Computer Engineering COMSATS University IslamabadLahore Campus45550Pakistan School of Engineering and Technology National Textile UniversityFaisalabad37610Pakistan Department of Electrical Engineering Information Technology UniversityLahore54000Pakistan Department of Electrical Engineering GC UniversityFaisalabad38000Pakistan Faculty of Mechanical and Industrial Engineering Warsaw University of Technology02-524WarsawPoland

wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by ***,they are exceedingly unpredictable since they rely highly on weather and atmospheric *** 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 *** 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 *** 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 *** of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization ***,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 *** these models were also optimized and modeled in *** 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.

关键词： Deep stacked autoencoder sequence to sequence autoencoder bidirectional long short-term memory network wind speed forecasting solar irradiation forecasting

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

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Journal of Central South University 2009年第4期16卷 690-696页

作者：刘辉田红旗李燕飞 Key Laboratory of Traffic Safety on Track Ministry of Education Central South University School of Traffic and Transportation Engineering Central South University

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

关键词： train safety wind speed forecasting wavelet analysis time series analysis Kalman filter optimization algorithm

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

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Transportation Safety and Environment 2023年第4期5卷 17-28页

作者： Bin Liu Xinmin Pan Rui Yang Zhu Duan Ye Li Shi Yin Nikolaos Nikitas Hui Liu Research Institute of Science and Technology of China Railway Urumqi Group Co. Ltd.Urumqi 830011XinjiangChina Xinjiang Uygur Autonomous Region Meteorological Service Urumqi 830002XinjiangChina School of Traffic and Transportation Engineering Central South UniversityChangsha 410075HunanChina School of Civil Engineering University of LeedsLS29JTLeedsUK

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 *** prediction of crosswinds can provide scientific guidance for safe train *** obalong the high-speed *** method consists of three parts:the data preprocessing module,the hybrid prediction module and original wind speed ***,Broyden-Fletcher-Goldfarb-Shanno(BFGS)method,non-linear autoregressive network with exoge-the reinforcement learing ensemble ***,fast ensemble empirical model decomposition(FEEMD)is used to process the prediction models for all the sublayers of ***,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 *** real wind speed data of two railway stations in Xinjiang are used for experimental *** show that compared with the single benchmark model,the hybrid ensemble model has better accumacy and robustness for wind speed prediction along 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/*** proposed ensemble model is a promising method for railway wind speed prediction.

关键词： wind speed forecasting high-speed railways signal decomposition reinforcement learning ensemble model

Improving the Forecasts of Coastal wind speeds in Tianjin,China Based on the WRF Model with Machine Learning Algorithms

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Journal of Meteorological Research 2024年第3期38卷 570-585页

作者： Weihang ZHANG Meng TIAN Shangfei HAI Fei WANG Xiadong AN Wanju LI Xiaodong LI Lifang SHENG College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao266100China Tianjin Key Laboratory for Oceanic Meteorology Tianjin Institute of Meteorological ScienceTianjin300074China CMA Earth System Modeling and Prediction Centre China Meteorological Administration(CMA)Beijing100081China Qingdao Meteorological Bureau Qingdao266003China

Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to *** the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind *** this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for *** results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing *** back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the *** regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)*** addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind *** study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.

关键词： machine learning Weather Research and Forecast(WRF)model wind speed forecasting coastal region

Short Term wind Power Prediction Using Wavelet Transform and ARIMA

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Journal of Energy and Power Engineering 2012年第11期6卷 1786-1790页

作者： In-Yong Seo Bok-Nam Hat Sang-Ok Kin Won Nam-Koong Dong-Wan Seo Seong-JunKim KEPCO Research Institute Smart Energy Lab. Daejeon 305-380 South Korea Department of lndustrial Information and Management Engineering Gangneung-Wonju National University Gangneung 210-702South Korea

A sustainable production of electricity is essential for low carbon green growth in South Korea. Although wind energy is unlimited in potential, both intermittency and volatility should be tackled for smart grid integration in future. To cope with this, many works have been done for wind speed and power forecasting. It is shown that statistical techniques are useful for short-term forecasting of wind power. This paper presents a statistical wind speed forecasting. The wavelet decomposition is employed as a de-noising technique. An illustration will be given by real-world dataset. According to the result, the MAD （mean absolute deviation） is improved as much as 10%.

关键词： wind speed forecasting autoreressive model wavelet decomnosition mean absolute deviation.