EspañolEnglish
Research Research projects EFIS: Adaptive Intelligent System for Energy Efficient Energy Management in Large Buildings

EFIS: Adaptive Intelligent System for Energy Efficient Energy Management in Large Buildings

MICINN. INNPACTO. IPT-2011-0962-920000-AR
    |

Energy saving has become a major issue worldwide as it has great impact on the economy and environment. Many governments are taking action to reduce dependence on fossil fuels. The pillars of this new model are regional cooperation, energy efficiency, and the development of renewable energies.

EFIS aims to develop this opportunity and make energy management efficient for large public buildings such as hospitals and government centres, as well as large office buildings and hotels. According to the International Energy Agency, buildings currently account for 40% of energy consumption in most countries.
The main objective of EFIS is to develop a software system for efficient energy management (EEM) in large buildings that automatically adapts to the characteristics of the building and changing environmental conditions.
EFIS is geared to large buildings where the comfort of the users is a key factor (hospitals, government buildings, hotels, and office buildings). EFIS tackles efficient energy management (EEM) in innovative ways, using advanced automatic learning techniques to create a consistent mathematical model by analysing large data sets collected from hundreds of field devices and sensors installed in the building.

One of the innovative characteristics of EFIS is the ability to continually adapt to environmental conditions:

  • weather conditions
  • conditions of use of the building, such as the number of people in a given area, planned use of rooms, and so on
  • Conditions of maintenance related to insulation and equipment performance

In addition to offering a tool for EEM, this project has a more ambitious target: the achievement of a qualitative leap in optimisation practices for energy management in large infrastructures. This means the use by auditing experts of mass data processing and artificial intelligence techniques to establish fresh recommendations and discover new ways of operating a facility while optimising energy consumption.