The FIDIA pilot in Rome (Italy) has two swimming pools, one volleyball indoor court, one gym and two outdoor multi-purpose courts. The local generation of energy is provided by gas boilers and a Biomass Plant

The main automation based intervention performed in FIDIA entailed the pump control bleeding of the AHU and the fan control AHU of the pool, as well as the handling ventilation, hot water valves and dampers.

Before Sporte2 intervention, the pumps in FIDIA worked 24 hours a day which results in huge energy consumption. The pumps control FIDIA rule based scenario aims to use pumps only when it is needed. A schedule has been thus introduced first to ensure the pumps (PUMP1, PUMP2, PUMP3) availability during various courses which take place in the area.

The best practise values initially defined through literature and normative in rules of FIDIA pump control scenario, were adjusted and tuned, after their implementation in the given facility, once a decent set of monitored results will be available to study the current behaviour of the system.

Case Study: FIDIA (Rome, Italy) 1 CUSP - Smart City Solution for Cardiff and Luxembourg

Electrical energy savings:

24%

Thermal energy savings:

34%

CO2 emission reduction:

29%
Case Study: FIDIA (Rome, Italy) 2 CUSP - Smart City Solution for Cardiff and Luxembourg

Related published papers:

  • Yuce, B., Li, H., Rezgui, Y., Petri, I., Jayan, B. and Yang, C. An Indoor Swimming Pool Case Study: Utilizing Artificial Neural Network Prediction to Achieve Better Energy Saving and Comfort Level, Energy and Buildings, http://dx.doi.org/10.1016/j.enbuild.2014.04.052, 2014, (Impact Factor: 3.254)
  • Petri, I., Li, H., Rezgui, Y., Chunfeng, Y., Yuce, B. and Jayan, B. A HPC based cloud model for real time energy optimization, Enterprise Information Systems, DOI: 10.1080/17517575.2014.919053, 2014, 2012, (Impact Factor: 9.2).
  • Petri, I., Li, H., Rezgui, Y., Chunfeng, Y., Yuce, B. and Jayan, B. A Modular optimization model for reducing energy consumption in large scale building facilities, Renewable & Sustainable Energy Reviews, https://doi.org/10.1016/j.rser.2014.07.044, 2014 (Impact Factor: 6.577)
  • Yang, C., Li, H., Rezgui, Y., Petri, I., Yuce, B., Chen, B. and Jayan, B. High Throughput Computing based Distributed Genetic Algorithm for Building Energy Consumption Optimization, Energy and Buildings, DOI: 10.1016/j.enbuild.2014.02.053, 2014, (Impact Factor: 3.254).