SELF (St. Maria de Lamas, Portugal) 1 CUSP (Computational Urban Sustainability Platform)

The SELF Santa Maria de Lamas pilot (Portugal)  has two swimming pools and two gyms. The pilot has three systems for the generation of hot water: solar system, CHP, boilers.

In SELF pilot, the swimming pool area where the sensor network optimization has been implemented into, has an external wall that is completely glazed with shading while the opposite side includes bleachers. The air heating and ventilation system is composed by two Air Handling Units providing the air supply and return through a distribution system. The module optimises supplied thermal energy and fan electrical consumption to achieve the optimal indoor thermal conditions minimising the energy used for the air treatment.

Facilities covered:
  • Olympic Pool (indoor) size: 50m x 25m, depth: 1,20 to 1,80, Capacity: 1650m3
  • Learning Pool (indoor) size: 20m x 6m, depth: 1,1m, Capacity: 132 m3
  • 2 Gyms (indoor) with one provided of electric equipment, such as electric bicycles
  • Water Heating for pools and showers
  • Air Heating for large environments
  • Electric Energy for indoor/outdoor lighting and electric equipment

Electrical energy savings:


Thermal energy savings:


Electrical CO2 reduction:


Thermal energy CO2 reduction:

SELF (St. Maria de Lamas, Portugal) 2 CUSP (Computational Urban Sustainability Platform)

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, DOI: 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, DOI: 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)

CUSP is Your Opportunity to Rethink Your City! Stop overconsuming and Achieve Net Zero!