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Deep Learning Based Building Load Prediction for Residences of an Academic Institutions

Effective energy planning has in many instances been identified as a critical operation in order to achieve economic and sustainable building energy usage. Accurate building load prediction is a key factor that can result in energy usage and cost reduction. This paper presents an energy usage analysis for an academic institution over a period of four years. Building load prediction was then considered, where three different deep learning models were implemented in an attempt to identify the model that would perform best at predicting the load demand of a selected building over a 1 year period. The accuracy of the implemented models was evaluated through analysis of the Mean Absolute Percentage Error. The selected models produced values ranging from 0.09 to 0.22, which compare well with results highlighted in other literature studies for similar buildings. Furthermore, the results showed that the Simple RNN exhibits better performance at uncovering the relationship between feature space variables and the response variable in comparison to the dense FFNN and the hybrid model