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Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review

机译:使用常规方法和人工智能方法进行建筑能耗预测分析:回顾

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摘要

It is important for building owners and operators to manage the electrical energy consumption of their buildings. As electrical energy is the major form of energy consumed in a commercial building, the ability to forecast electrical energy consumption in a building will bring great benefits to the building owners and operators. This paper provides a review of the building electrical energy consumption forecasting methods which include the conventional and artificial intelligence (AI) methods. The significant goal of this study is to review, recognize, and analyse the performance of both methods for forecasting of electrical energy consumption. Compared to using a single method of forecasting, the hybrid of two forecasting methods can possibly be applied for more precise results. Regarding this potential, the swarm intelligence (SI) method has been reviewed to be hybridized with AI. Published literature presented in this paper shows that, the hybrid of SVM and SI methods has indeed presented superior performance for forecasting building electrical energy consumption.
机译:对于建筑物所有者和运营商来说,管理建筑物的电能消耗非常重要。由于电能是商业建筑中消耗能量的主要形式,因此预测建筑物中电能消耗的能力将为建筑物所有者和运营商带来巨大利益。本文概述了建筑电能消耗的预测方法,其中包括常规方法和人工智能(AI)方法。这项研究的重要目标是审查,识别和分析两种用于预测电能消耗的方法的性能。与使用单一预测方法相比,两种预测方法的混合可能会应用于更精确的结果。关于这种潜力,已经研究了群体智能(SI)方法与AI混合的方法。本文提供的公开文献表明,SVM和SI方法的混合确实在预测建筑物的电能消耗方面表现出了卓越的性能。

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  • 来源
    《Renewable & Sustainable Energy Reviews》 |2017年第4期|1108-1118|共11页
  • 作者单位

    Univ Teknol Malaysia, CEES, Inst Future Energy, Johor Baharu 81310, Johor, Malaysia|Univ Teknol Malaysia, Fac Elect Engn, Johor Baharu 81310, Johor, Malaysia;

    Univ Teknol Malaysia, CEES, Inst Future Energy, Johor Baharu 81310, Johor, Malaysia|Univ Teknol Malaysia, Fac Elect Engn, Johor Baharu 81310, Johor, Malaysia;

    Univ Teknol Malaysia, CEES, Inst Future Energy, Johor Baharu 81310, Johor, Malaysia|Univ Teknol Malaysia, Fac Mech Engn, Johor Baharu 81310, Johor, Malaysia;

    Univ Teknol Malaysia, CEES, Inst Future Energy, Johor Baharu 81310, Johor, Malaysia|Univ Teknol Malaysia, Fac Elect Engn, Johor Baharu 81310, Johor, Malaysia;

    Univ Teknol Malaysia, CEES, Inst Future Energy, Johor Baharu 81310, Johor, Malaysia|Univ Teknol Malaysia, Fac Elect Engn, Johor Baharu 81310, Johor, Malaysia;

    Univ Teknol Malaysia, CEES, Inst Future Energy, Johor Baharu 81310, Johor, Malaysia|Univ Teknol Malaysia, Fac Elect Engn, Johor Baharu 81310, Johor, Malaysia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Decision making; Electrical energy consumption forecasting; Artificial intelligence;

    机译:决策;电力能耗预测;人工智能;

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