Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks

Donghun Lee, Jongeun Kim, Suhee Kim and Kwanho Kim ()
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Donghun Lee: Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea
Jongeun Kim: Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea
Suhee Kim: Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea
Kwanho Kim: Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea

Energies, 2023, vol. 16, issue 24, 1-13

Abstract: As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model’s performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption.

Keywords: smart energy systems; building energy management systems; energy forecasting; deep recurrent neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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