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A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Authors
Moon, JihoonPark, SungwooRho, SeungminHwang, Eenjun
Issue Date
9월-2019
Publisher
SAGE PUBLICATIONS INC
Keywords
Short-term load forecasting; building energy consumption forecasting; artificial neural network; hyperparameter tuning; scaled exponential linear unit
Citation
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, v.15, no.9
Indexed
SCIE
SCOPUS
Journal Title
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Volume
15
Number
9
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/63039
DOI
10.1177/1550147719877616
ISSN
1550-1329
Abstract
Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network-based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network-based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.
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Hwang, Een jun
공과대학 (전기전자공학부)
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