  In this paper we propose a Long Short-Term Memory Network based method to
forecast the energy consumption in public buildings, based on past
measurements. Our approach consists of three main steps: data processing step,
training and validation step, and finally the forecasting step. We tested our
method on a data set consisting of measurements taken every half an hour from
the main building of the National Archives of the United Kingdom, in Kew and as
evaluation metrics we have used Mean Absolute Error (MAE) and Mean Absolute
Percentage Error (MAPE).
