A Predictive Model for Electricity Consumption in University Campuses using Artificial Neural Networks (A Case Study)
Energy efficiency is paramount in the quest to achieve sustainable development in the 21st century. Statistics in recent research have shown that in many sectors in any nation’s economy, which include buildings, industries and transportation, energy consumption in buildings accounts for about 77%, a higher percentage than other sectors in Nigeria; the same is true worldwide. Energy consumption forecasting is a critical and necessary input to planning and monitoring energy usage, with particular reference to CO 2 and other greenhouse gas emissions. According to literature, very little research has been carried out in designing models for energy consumption in institutional buildings. In this research, the African University of Science and Technology (AUST) is considered as a case study, whereby the data collected is the monthly energy consumption for the period 2012–2014 and 2015–2017. The data was collected from the monthly electricity utility bills when the school was using a flat rate and when they were using a measured meter rating respectively. The two models were designed for the monthly prediction of electricity consumption of the buildings within the university using an artificial neural network. Results obtained from the two models were compared and showed that the model designed using the latter dataset could be adopted to forecast the electricity consumption of the school with respect to its population. This will further assist the university in monitoring the trends of energy consumption, classify factors and components that impact energy consumption within the university community and hence building policies on its usage and consumption. Moreover the possibility of using renewable energy in the university could also be integrated as a future work.