Application of Short Term Energy Consumption Forecasting for Household Energy Management System

被引：0
|
作者：
Ahmed, K. M. U. [1 ]
Amin, M. A. Ai [1 ]
Rahman, M. T. [1 ]
机构：
[1] UAP, Dept Elect & Elect Engn EEE, Dhaka, Bangladesh
来源：
2015 3RD INTERNATIONAL CONFERENCE ON GREEN ENERGY AND TECHNOLOGY (ICGET) | 2015年
关键词：
Artificial Neural Network (ANN); machine learning; short-term load forecasting (STLF); storage device scheduling; NEURAL-NETWORK;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In the context of the smart grid, energy management systems at household level has a vital impact on distribution grid. PV based energy systems at household level become more popular day-by-day. Thus scheduling residential energy storage device is necessary to optimize technical and market integration of distributed energy resources (DERs), especially the ones based on renewable energy. The first step of electricity consumption forecasting at individual household level is used to achieve proper scheduling of the storage devices. Then an intelligent agent based controlling technique is proposed to make sure the financial benefits of end-user as a part of energy management system. In this paper the forecasting ability of Artificial Neural Network (ANN) is evaluated to capture the daily electricity consumption profile of an individual household.
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页数：6
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