Load Forecasting Using Neural Networks and Blockchains for Low Voltage Distribution Networks

被引：1

作者：

Qaisieh, Lauren ^{[1
]}

Tawalbeh, Nabeel ^{[1
]}

机构：

[1] Univ Jordan, Elect Engn Dept, Amman, Jordan

来源：

2022 6TH INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA 2022) | 2022年

关键词：

neural network; blockchain; load forecasting; short -term load forecasting;

D O I：

10.1109/ICGEA54406.2022.9791860

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This work presents an efficient and reliable short term load forecasting algorithm that can determine the optimal operating conditions for end consumers. The proposed solution combines smart meters readings from the electrical distribution company along with weather conditions, and previous consumption figures and feeds them into a reliable neural network-based load forecasting and scheduling algorithm to ensure optimal operating conditions. Predicted power consumption figures will be presented to end users to plan for optimal energy usage. Furthermore, A Blockchain-based communication system is used for information exchange throughout the network. End users will have their actual power consumption figures sent to the utility provider to ensure the planned operation and efficient use of power plants and implementation of dynamic pricing.

引用

页码：210 / 214

页数：5

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