Sustainable Investment Forecasting of Power Grids Based on theDeep Restricted Boltzmann Machine Optimized by the Lion Algorithm

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.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

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.

Calibration of Gridded Wind Speed Forecasts Based on Deep Learning

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.