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.
Highlights
The rapid economic growth and the increasing population in developing countries are causing the global energy consumption to increase
Results obtained show that Machine Learning approaches could achieve better results, in particular methods based on bagging and boosting ensemble schemes obtained the best results
We describe the Machine Learning techniques used in our empirical study
Summary
The rapid economic growth and the increasing population in developing countries are causing the global energy consumption to increase. Despite efficiency gains over the last decades, it is estimated that by 2040 the global energy demand will likely increase nearly 25 percent [1]. According to the International Energy Agency (IEA) [2], developing regions are expected to experience large demand growth between 2017 and 2040, especially countries from Asia and Africa. CO2 emissions from energy will rise around 10% (from 32.6 to 35.9 gigatons). While in developed countries CO2 emissions are expected to drop by 23%, in developing regions they are supposed to rise by 27%. The reduction of CO2 emissions has become the focus of international political, economic and environmental research
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