A Performance-Driven Economic Analysis of a LSTM Neural Network Used for Predicting Building Energy Consumption

The energy sector occupies a central role in European policy regarding the sustainability and climate change ambitions. This economic sector gained even more importance in the context of a growing energy demand and the threat to energy security and stability in Eastern Europe. The administrative changes from the past years and the shift towards renewable energy sources created the momentum for a general transformation of the energy sector. The research in this field is much needed in order to find the best solutions to predict the energy demand and how to efficiently satisfy it. The purpose of this paper is to assess the economic impact of using artificial neural networks on a dataset that captures information from one building about the energy consumption. The network follows a Long – Short Term Memory architecture and it is characterized by a Root Mean Squared Propagation function for optimization. The analysis consists in the comparison of the performance of three activation functions: Rectified Linear Unit, Sigmoid and Tanh. The results complement the existing research in the field by focusing on the prediction of energy consumption at building level. The case study indicates that the Rectified Linear Unit and Tanh functions are more appropriate than Sigmoid to be used in an LSTM network applied on energy consumption data. The former performs better in terms of accuracy, measured by the Mean Absolute Value, and has similar computational costs to Tanh, with a slightly larger value for training time.