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Data Mining and Machine Learning in Building Energy Analysis by Hai-Xiang Zhao, Frédéric Magoules

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1Overview of Building Energy Analysis

1.1. Introduction

In Europe, buildings account for 40% of total energy use and 36% of total CO2 emission [EUR 10]. Figure 1.1 shows the annual energy consumption of each sector over 20 years from 1990 to 2009 in France. The part of industry decreased from 30% to 25%, and that of transport was stable around 30%. However, the usage of residential tertiary increased from 37% to 41%. We can see an increasing ratio of the building energy consumption during these years, and we can expect that the ratio will continue to increase in the future. The prediction of energy use in buildings is therefore significant for improving the energy performance of buildings, leading to energy conservation and reducing environmental impact.

However, the energy system in buildings is quite complex, as the energy types and building types vary greatly. In the literature, the main energy forms considered are heating/cooling loads, hot water and electricity consumption. The most frequently considered building types are offices, residential and engineering buildings, varying from small rooms to big estates. The energy behavior of a building is influenced by many factors, such as weather conditions, especially the dry bulb temperature, building construction and thermal property of the physical materials used, occupants and their behavior, sublevel components such as heating, ventilating and air conditioning (HVAC), and lighting systems.

Due to the complexity of the energy ...

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