Energy Consumption of a Building by using Long Short-Term Memory Network: A Forecasting Study

Abstract

Buildings have a dominant presence in energy consumption for the transition to clean energy. During 2017, construction and operation of buildings worldwide represented more than a third (36%) of final energy used and 40% of the emissions of carbon dioxide. Hence, there is great interest in reducing energy use in this sector, and energy efficiency in buildings to enhance energy performances is a suitable way. In this paper, black-box approaches based on artificial neural networks to predict the electric load of a selected educational building are proposed. The potential and robustness of long short-term memory (LSTM) applied to a dataset with a limited number of days of observations are analyzed. The results in our scenario showed that the LSTM surpasses in accuracy to other techniques such as feed-forward neural networks.