Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Mengchen Zhao, Santiago Gomez-Rosero, Hooman Nouraei, Craig Zych, Miriam A. M. Capretz and Ayan Sadhu ()
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Mengchen Zhao: Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada
Santiago Gomez-Rosero: Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada
Hooman Nouraei: Neelands Group Ltd., Burlington, ON L7M 0V9, Canada
Craig Zych: Neelands Group Ltd., Burlington, ON L7M 0V9, Canada
Miriam A. M. Capretz: Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada
Ayan Sadhu: Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada

Energies, 2024, vol. 17, issue 7, 1-24

Abstract: Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

Keywords: commercial building energy consumption; peak demand; timestamp prediction; deep learning; MLP; LSTM (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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