Open Access

ARTICLE

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

Qian Wang¹, Xiaolong Yang^2,*, Di Pu³, Yingying Fan⁴

1 School of Economics and Management, Jilin Institute of Chemical Technology, Jilin, 132022, China
2 School of Economics and Management, Northeast Electric Power University, Jilin, 132012, China
3 Digital Grid Research Institute, China Southern Power Grid, Guangzhou, 510000, China
4 School of Art, Hebei GEO University, Shijiazhuang, 050031, China

* Corresponding Author: Xiaolong Yang. Email:

(This article belongs to the Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)

Computer Modeling in Engineering & Sciences 2022, 130(1), 269-286. https://doi.org/10.32604/cmes.2022.016437

Received 06 March 2021; Accepted 26 July 2021; Issue published 29 November 2021

Abstract

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-DRBM model compensates for the deficiency of the single model, 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.

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Keywords

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