Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/243688 
Year of Publication: 
2019
Citation: 
[Journal:] Energy Reports [ISSN:] 2352-4847 [Volume:] 5 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1509-1524
Publisher: 
Elsevier, Amsterdam
Abstract: 
The rise in environmental awareness has increased the significance of controlling and monitoring electricity consumption. The efficiency of power management is directly affected by the accuracy of predicting electricity consumption. It is easy to estimate the electricity consumption if the electricity status is predicted. Therefore, this study proposes a method to predict the electricity consumption of public buildings by using an adaptive network-based fuzzy inference systems (ANFISs) and weather conditions. ANFIS combines the interpretability of fuzzy inference systems and the learning ability of neural networks. Gray relational analysis (GRA) is used to analyze the relationship between weather conditions and electricity consumption. In this study, a multi-ANFISs approach is introduced to estimate the electricity consumption by weather conditions and human activities. An alarm system was also developed using the estimation errors. The results show that the proposed multi-ANFISs achieves a greater performance with less number of parameters, and the GRA can evaluate the magnitude of relation between the factors and a specific output.
Subjects: 
ANFIS
Electricity consumption
GRA
Prediction
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
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