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A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings

Federico Divina (), Miguel García Torres (), Francisco A. Goméz Vela () and José Luis Vázquez Noguera ()
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Federico Divina: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Miguel García Torres: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Francisco A. Goméz Vela: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
José Luis Vázquez Noguera: Ingeniería Informática, Universidad Americana, Asunción PY-1429, Paraguay

Energies, 2019, vol. 12, issue 10, 1-23

Abstract: Smart buildings are equipped with sensors that allow monitoring a range of building systems including heating and air conditioning, lighting and the general electric energy consumption. Thees data can then be stored and analyzed. The ability to use historical data regarding electric energy consumption could allow improving the energy efficiency of such buildings, as well as help to spot problems related to wasting of energy. This problem is even more important when considering that buildings are some of the largest consumers of energy. In this paper, we are interested in forecasting the energy consumption of smart buildings, and, to this aim, we propose a comparative study of different forecasting strategies that can be used to this aim. To do this, we used the data regarding the electric consumption registered by thirteen buildings located in a university campus in the south of Spain. The empirical comparison of the selected methods on the different data showed that some methods are more suitable than others for this kind of problem. In particular, we show that strategies based on Machine Learning approaches seem to be more suitable for this task.

Keywords: time series forecasting; electric energy consumption forecasting; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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