Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher

被引：192

作者：

Metaxiotis, K ^{[1
]}

Kagiannas, A ^{[1
]}

Askounis, D ^{[1
]}

Psarras, J ^{[1
]}

机构：

[1] Natl Tech Univ Athens, Dept Elect & Comp Engn, GR-15773 Athens, Greece

来源：

ENERGY CONVERSION AND MANAGEMENT | 2003年 / 44卷 / 09期

关键词：

artificial intelligence; electric load forecasting; energy;

D O I：

10.1016/S0196-8904(02)00148-6

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. AI-based systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. This paper provides an overview for the researcher of AI technologies, as well as their current use in the field of short term electric load forecasting (STELF). The history of AI in STELF is outlined, leading to a discussion of the various approaches as well as the current research directions. The paper concludes by sharing thoughts and estimations on AI future prospects in this area. This review reveals that although still regarded as a novel methodology, AI technologies are shown to have matured to the point of offering real practical benefits in many of their applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：1525 / 1534

页数：10

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