Short-term Forecasting for Integrated Load and Renewable Energy in Micro-grid Power Supply

被引：0

作者：

Mwanza, Naomi Nthambi ^{[1
]}

Moses, Peter Musau ^{[1
]}

Nyete, Abraham Mutunga ^{[1
]}

机构：

[1] Univ Nairobi, Dept Elect & Informat Engn, Nairobi, Kenya

来源：

2020 IEEE PES & IAS POWERAFRICA CONFERENCE | 2020年

关键词：

Artificial Neural Network; Enhanced Particle Swamp Optimization; Load Forecasting; Renewable Energy Forecasting; WIND;

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：

For planning and operation activities, accurate forecasting of demand is very important in sustaining the load demand in the electrical power system. Recently there has been increased use of renewable energy and unlike other sources of electricity like diesel generators, estimation of power production from renewable sources is uncertain. Therefore, reliable techniques for forecasting renewable energy and load demand are of paramount importance. Several forecasting techniques have been researched on in the past and are classified into; physical, statistical and Al techniques The proposed research involves forecasting integrated load and renewable energy (solar and wind) using Artificial Neural Network(ANN) and Enhanced Particle Swamp Optimization (EPSO) techniques. The output of this research is the predicted netload. The analysis of the results depicts ANN_EPSO as a reliable method for forecasting renewable energy and Load demand.

引用

页数：5

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