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

Jihoon Moon, Sungwoo Park, Seungmin Rho and Eenjun Hwang

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 9, 1550147719877616

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.

Keywords: Short-term load forecasting; building energy consumption forecasting; artificial neural network; hyperparameter tuning; scaled exponential linear unit (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:9:p:1550147719877616

DOI: 10.1177/1550147719877616

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