The challenges of applying deep learning(DL) to correct deterministic numerical weather prediction(NWP) biases with non-Gaussian distributions are discussed in this paper.It is known that the DL UNet model is incapabl...The challenges of applying deep learning(DL) to correct deterministic numerical weather prediction(NWP) biases with non-Gaussian distributions are discussed in this paper.It is known that the DL UNet model is incapable of correcting the bias of strong winds with the traditional loss functions such as the MSE(mean square error),MAE(mean absolute error),and WMAE(weighted mean absolute error).To solve this,a new loss function embedded with a physical constraint called MAE_MR(miss ratio) is proposed.The performance of the UNet model with MAE_MR is compared to UNet traditional loss functions,and statistical post-processing methods like Kalman filter(KF) and the machine learning methods like random forest(RF) in correcting wind speed biases in gridded forecasts from the ECMWF high-resolution model(HRES) in East China for lead times of 1–7 days.In addition to MAE for full wind speed,wind force scales based on the Beaufort scale are derived and evaluated.Compared to raw HRES winds,the MAE of winds corrected by UNet(MAE_MR) improves by 22.8% on average at 24–168 h,while UNet(MAE),UNet(WMAE),UNet(MSE),RF,and KF improve by 18.9%,18.9%,17.9%,13.8%,and 4.3%,respectively.UNet with MSE,MAE,and WMAE shows good correction for wind forces 1–3 and 4,but negative correction for 6 or higher.UNet(MAE_MR) overcomes this,improving accuracy for forces 1–3,4,5,and 6 or higher by 11.7%,16.9%,11.6%,and 6.4% over HRES.A case study of a strong wind event further shows UNet(MAE_MR) outperforms traditional post-processing in correcting strong wind biases.展开更多
This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the Lion algorithm(LA).Firstly,two factors including transmission and distribution pric...This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the Lion algorithm(LA).Firstly,two factors including transmission and distribution price reform(TDPR)and 5G station construction were comprehensively incorporated into the consideration of influencing factors,and the fuzzy threshold method was used to screen out critical influencing factors.Then,the LA was used to optimize the parameters of the DRBM model to improve the model’s prediction accuracy,and the model was trained with the selected influencing factors and investment.Finally,the LA-DRBM model was used to predict the investment of a power grid enterprise,and the final prediction result was obtained by modifying the initial result with the modifying factors.The LA-DRBMmodel compensates for the deficiency of the singlemodel,and greatly improves the investment prediction accuracy of the power grid.In this study,a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model,and a comparison with the RBM,support vector machine(SVM),back propagation neural network(BPNN),and regression model was conducted to verify the superiority of the model.The conclusion indicates that the proposed model has a strong generalization ability and good robustness,is able to abstract the combination of low-level features into high-level features,and can improve the efficiency of the model’s calculations for investment prediction of power grid enterprises.展开更多
Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented ...Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.展开更多
This study investigates the data requirements of generative artificial intelligence(AI),particularly generative adversarial networks(GANs),for reliable data augmentation in energy applications.Generative AI,though see...This study investigates the data requirements of generative artificial intelligence(AI),particularly generative adversarial networks(GANs),for reliable data augmentation in energy applications.Generative AI,though seen as a solution to data limitations,requires substantial data to learn meaningful distributions—a challenge often overlooked.This study addresses the challenge through synthetic data generation for critical heat flux(CHF)and power grid demand,focusing on renewable and nuclear energy.Two variants of GAN employed are conditional GAN(cGAN)and Wasserstein GAN(wGAN).Our findings include the strong dependency of GAN on data size,with performance declining on smaller datasets and varying performance when generalizing to unseen experiments.Mass flux and heated length significantly influence CHF predictions.wGAN is more robust to feature exclusion,making it suitable for constrained synthetic data generation.In energy demand forecasting,wGAN performed well for solar,wind,and load predictions.Longer lookback hours and larger datasets improved predictions,especially for load power.Seasonal variations posed challenges,with wGAN achieving a relatively high error of Root Mean Squared Error(RMSE)of 0.32 for load power prediction,compared to RMSE of 0.07 under same-season conditions.Feature exclusions impacted cGAN the most,while wGAN showed greater robustness.This study concludes that,while generative AI is effective for data augmentation,it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.展开更多
基金Supported by the National Key Research and Development Program of China (2021YFC3000905)Key Innovation Team Fund of China Meteorological Administration (CMA2022ZD04)。
文摘The challenges of applying deep learning(DL) to correct deterministic numerical weather prediction(NWP) biases with non-Gaussian distributions are discussed in this paper.It is known that the DL UNet model is incapable of correcting the bias of strong winds with the traditional loss functions such as the MSE(mean square error),MAE(mean absolute error),and WMAE(weighted mean absolute error).To solve this,a new loss function embedded with a physical constraint called MAE_MR(miss ratio) is proposed.The performance of the UNet model with MAE_MR is compared to UNet traditional loss functions,and statistical post-processing methods like Kalman filter(KF) and the machine learning methods like random forest(RF) in correcting wind speed biases in gridded forecasts from the ECMWF high-resolution model(HRES) in East China for lead times of 1–7 days.In addition to MAE for full wind speed,wind force scales based on the Beaufort scale are derived and evaluated.Compared to raw HRES winds,the MAE of winds corrected by UNet(MAE_MR) improves by 22.8% on average at 24–168 h,while UNet(MAE),UNet(WMAE),UNet(MSE),RF,and KF improve by 18.9%,18.9%,17.9%,13.8%,and 4.3%,respectively.UNet with MSE,MAE,and WMAE shows good correction for wind forces 1–3 and 4,but negative correction for 6 or higher.UNet(MAE_MR) overcomes this,improving accuracy for forces 1–3,4,5,and 6 or higher by 11.7%,16.9%,11.6%,and 6.4% over HRES.A case study of a strong wind event further shows UNet(MAE_MR) outperforms traditional post-processing in correcting strong wind biases.
基金the National Key Research and Development Program of China(Grant No.2020YFB1707804)the 2018 Key Projects of Philosophy and Social Sciences Research(Grant No.18JZD032)Natural Science Foundation of Hebei Province(Grant No.G2020403008).
文摘This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the Lion algorithm(LA).Firstly,two factors including transmission and distribution price reform(TDPR)and 5G station construction were comprehensively incorporated into the consideration of influencing factors,and the fuzzy threshold method was used to screen out critical influencing factors.Then,the LA was used to optimize the parameters of the DRBM model to improve the model’s prediction accuracy,and the model was trained with the selected influencing factors and investment.Finally,the LA-DRBM model was used to predict the investment of a power grid enterprise,and the final prediction result was obtained by modifying the initial result with the modifying factors.The LA-DRBMmodel compensates for the deficiency of the singlemodel,and greatly improves the investment prediction accuracy of the power grid.In this study,a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model,and a comparison with the RBM,support vector machine(SVM),back propagation neural network(BPNN),and regression model was conducted to verify the superiority of the model.The conclusion indicates that the proposed model has a strong generalization ability and good robustness,is able to abstract the combination of low-level features into high-level features,and can improve the efficiency of the model’s calculations for investment prediction of power grid enterprises.
文摘Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network （BP-ANN） based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid （China） based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.
基金supported through Idaho National Laboratory,United States’s Laboratory Directed Research and Development(LDRD)Program Award Number(24A1081-116FP)under Department of Energy Idaho Operations Office contract no.DE-AC07-05ID14517.
文摘This study investigates the data requirements of generative artificial intelligence(AI),particularly generative adversarial networks(GANs),for reliable data augmentation in energy applications.Generative AI,though seen as a solution to data limitations,requires substantial data to learn meaningful distributions—a challenge often overlooked.This study addresses the challenge through synthetic data generation for critical heat flux(CHF)and power grid demand,focusing on renewable and nuclear energy.Two variants of GAN employed are conditional GAN(cGAN)and Wasserstein GAN(wGAN).Our findings include the strong dependency of GAN on data size,with performance declining on smaller datasets and varying performance when generalizing to unseen experiments.Mass flux and heated length significantly influence CHF predictions.wGAN is more robust to feature exclusion,making it suitable for constrained synthetic data generation.In energy demand forecasting,wGAN performed well for solar,wind,and load predictions.Longer lookback hours and larger datasets improved predictions,especially for load power.Seasonal variations posed challenges,with wGAN achieving a relatively high error of Root Mean Squared Error(RMSE)of 0.32 for load power prediction,compared to RMSE of 0.07 under same-season conditions.Feature exclusions impacted cGAN the most,while wGAN showed greater robustness.This study concludes that,while generative AI is effective for data augmentation,it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.
