Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. (April 2017)

• Record Type: Journal Article
• Authors: Mat Daut, Mohammad Azhar; Hassan, Mohammad Yusri; Abdullah, Hayati; Rahman, Hasimah Abdul; Abdullah, Md Pauzi; Hussin, Faridah
• Is Part Of: Renewable & sustainable energy reviews. Volume 70(2017)
• Page Start: 1108
• Page End: 1118
• Publication Date: 2017-04
• Subjects: Decision making -- Electrical energy consumption forecasting -- Artificial intelligence; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.12.015
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: 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.