Load Forecasting with Hybrid Deep Learning Model for Efficient Power System Management

被引：0

作者：

Gochhait S. ^{[1
]}

Sharma D.K. ^{[2
]}

Rathore R.S. ^{[3
]}

Jhaveri R.H. ^{[4
]}

机构：

[1] Neurosciences Research Institute, Samara State Medical University, Russia Symbiosis Institute of Digital and Telecom Management, constituents of Symbiosis International Deemed University, Pune

[2] ISBM College of Engineering, Pune

[3] Department of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Llan-daff Campus, Western Avenue, Cardiff

[4] Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar

来源：

Recent Advances in Computer Science and Communications | 2024年 / 17卷 / 01期

关键词：

artificial intelligence; BI-LSTM; CNN; Energy management; pattern monitoring; STLF;

D O I：

10.2174/0126662558256168231003074148

中图分类号：

学科分类号：

摘要：

Aim: Load forecasting for efficient power system management. Background: Short-term energy load forecasting (STELF) is a valuable tool for utility compa-nies and energy providers because it allows them to predict and plan for changes in energy. Method: 1D CNN BI-LSTM model incorporating convolutional layers. Result: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation. © 2024 Bentham Science Publishers.

引用

页码：38 / 51

页数：13

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