Manuscript received March 31, 2023; revised April 26, 2023; accepted May 10, 2023.
Abstract—A smart city should ideally be environmentally
friendly and sustainable, and energy management is one
technique to monitor sustainable use. Similarly, this notion
might be applied in an urban form, such as the sort of city in
which a university would be located. This research analyzes the
possibility for a university to enhance energy management by
permitting the adoption of a variety of intelligent technologies
that increase the energy sustainability of a city's infrastructure
and the effectiveness of its operations. In the first module of the
proposed system, we place significant emphasis on the data
capabilities necessary to create energy statistics for each of its
various buildings. In the second phase of the technique, we
employ the collected data to conduct a data analysis of the
energy behavior inside micro-cities, from which we derive
characteristics. In the third module, we develop baseline
regressors to assess the varying degrees of efficacy of the
proposed model. Last, we describe a way for developing an
energy prediction model using a deep learning regression model
to solve the problem of short-term energy consumption
forecasting. The performance metric results show that the
suggested deep learning model increases performance prediction
compared to other traditional regression techniques. The
proposed model has superior RMSE, MAE and R squared
results compared to alternative regression models.
Index Terms—Deep learning, energy consumption, sustainable urban energy, sustainable smart campus
The authors are with the Department of Industrial and Management Engineering, Incheon National University, Republic of Korea.
*Correspondence: firstname.lastname@example.org (K.K.)
Cite: Berny Carrera and Kwanho Kim*, "Sustainable Smart University: A Short-Term Deep Learning Framework for Energy Consumption Forecast," International Journal of Machine Learning vol. 13, no. 4, pp. 146-152, 2023.Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).