Short Term Load Forecasting using Fuzzy Adaptive Inference and Similarity

被引：0

作者：

Jain, Amit ^{[1
]}

Srinivas, E. ^{[1
]}

Rauta, Rasmimayee ^{[1
]}

机构：

[1] IIIT Hyderabad, Power Syst Res Ctr, Hyderabad, Andhra Pradesh, India

来源：

2009 WORLD CONGRESS ON NATURE & BIOLOGICALLY INSPIRED COMPUTING (NABIC 2009) | 2009年

关键词：

Euclidean norm; fuzzy logic; optimization; short term load forecasting; similar days;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The main objective of short term load forecasting (STLF) is to provide load predictions for generation scheduling, economic load dispatch and security assessment at any time. Thus, STLF is needed to supply necessary information for the system management of day-to-day operations and unit commitment. This paper presents a forecasting method based on similar day approach in conjunction with fuzzy rule-based logic. To obtain the next-day load forecast, fuzzy logic is used to modify the load curves on selected similar days. A Euclidean norm considering weather variables such as 'temperature' and 'humidity' with weight factors is used for the selection of similar days. The effectiveness of the proposed approach is demonstrated on a typical load and weather data.

引用

页码：1742 / 1747

页数：6

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