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

Author:

Divina FedericoORCID,García Torres MiguelORCID,Goméz Vela Francisco A.ORCID,Vázquez Noguera José LuisORCID

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.

Funder

European Regional Development Fund

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

Reference66 articles.

1. 2018 Outlook for Energy: A View to 2040https://corporate.exxonmobil.com/en/~/media/Global/Files/outlook-for-energy/2018-Outlook-for-Energy.pdf.

2. World Energy Outlook 2018,2018

3. Regional estimates of the transient climate response to cumulative CO2 emissions

4. Energy 2020, A Strategy for Competitive, Sustainable and Secure Energyhttp://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52010DC0639&from=EN

5. Energy Efficiency: Buildingshttps://www.iea.org/topics/energyefficiency/buildings

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