Sustainable Smart University: A Short-Term Deep Learning Framework for Energy Consumption Forecast

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.