A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings
Autor: | Miguel García Torres, José Luis Vázquez Noguera, Francisco A. Goméz Vela, Federico Divina |
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Rok vydání: | 2019 |
Předmět: |
Control and Optimization
Computer science 020209 energy Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology Transport engineering Electric energy consumption Range (aeronautics) 0202 electrical engineering electronic engineering information engineering time series forecasting Electrical and Electronic Engineering Time series Engineering (miscellaneous) Building automation lcsh:T Renewable Energy Sustainability and the Environment business.industry Energy consumption machine learning electric energy consumption forecasting Air conditioning 020201 artificial intelligence & image processing business Energy (signal processing) Energy (miscellaneous) Efficient energy use |
Zdroj: | Energies, Vol 12, Iss 10, p 1934 (2019) Energies Volume 12 Issue 10 |
ISSN: | 1996-1073 |
DOI: | 10.3390/en12101934 |
Popis: | 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. |
Databáze: | OpenAIRE |
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