Deep Learning for Forecasting the Energy Consumption in Public Buildings
This work addresses energy management for public building operators, but it is incremental as it applies an existing method to a new dataset.
The authors tackled the problem of forecasting energy consumption in public buildings using a Long Short-Term Memory Network, achieving results evaluated with Mean Absolute Error and Mean Absolute Percentage Error on data from the National Archives of the United Kingdom.
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).