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

TLDR

Smart buildings use sensor data to monitor systems and energy use, and historical consumption records can improve efficiency and detect waste, which is critical given buildings' large energy consumption. The study aims to forecast smart building energy consumption by comparing various forecasting strategies. The authors evaluated the methods on electric consumption data from thirteen university campus buildings in southern Spain. The comparison revealed that machine‑learning based strategies outperform others for short‑term energy consumption forecasting in smart buildings.

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.