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arXiv · 2207.11953

Deep Learning for Forecasting the Energy Consumption in Public Buildings

Viorica Rozina Chifu·Cristina Bianca Pop·Emil St. Chifu·Horatiu Barleanu
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Abstract

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).

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BibTeXRIS

Viorica Rozina Chifu, Cristina Bianca Pop, Emil St. Chifu, Horatiu Barleanu. 2022-07-25. Deep Learning for Forecasting the Energy Consumption in Public Buildings. https://doi.org/10.1109/roedunet54112.2021.9638281

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