Artificial Neural Network Based Short Term Power Demand Forecast for Smart Grid

被引：0

作者：

Kulkarni, Sonali N. ^{[1
]}

Shingare, Prashant ^{[2
]}

机构：

[1] Univ Mumbai, Rajiv Gandhi Inst Technol, Elect & Telecom Engn, Mumbai 400053, Maharashtra, India

[2] Vertiv Energy Pvt Ltd, Renewable Energy, NITCO Business Pk,Wagle Ind Estate, Thane W 400604, Maharashtra, India

来源：

2018 IEEE CONFERENCE ON TECHNOLOGIES FOR SUSTAINABILITY (SUSTECH) | 2018年

关键词：

demand supply balance; demand forecasting; power quality; renewable energy grid integration; smart grid;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Globally, utilization of distributed renewable energy (RE) generators along with conventional one are remarkably increasing; to meet exponential rise in power demand, due to increased automation and industrialization. To handle challenges invoked due to increased number of distributed renewable energy generators in power network Smart Grid or smart power network is needed. Most important objective for smart power system or Smart Grid is demand supply balance to ensure stable, reliable and economical operation of power system. Short term demand forecast information is useful for real time operation and control of power system. In this paper we have discussed and presented Artificial Neural Network (ANN) based short term power demand forecast models, designed using historical hourly power demand data from Maharashtra state of India. The designed ANN based short term power demand forecast models can be deployed in renewable energy smart grid integration.

引用

页码：1 / 7

页数：7

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