AI Energy Optimization Solution (“AI-EOS”) for HVAC System

I&T Solution AI Energy Optimization Solution (“AI-EOS”) for HVAC System
(REF : S-0219)
Matched I&T Wish Energy management system on air side system with Artificial Intelligence Algorithms
(REF: W-0208)
Trial Project P-0098
P-0096
Solution Feature
  • The objective of AI Energy Optimization Solution (“AI-EOS”) is to manage and optimize HVAC plants from a global point of view, and the re-commissioning process will be executed to search for energy saving opportunities.
  • AI-EOS is different to the traditional BMS, where its focus usually on monitoring, scheduling and optimizing set of HVAC equipment independently.
  • AI-EOS is to optimize the HVAC System with taking the consideration of the overall plant efficiency and the building cooling/heat load with the outdoor condition and energy input to the associated chillers/heat pumps/chilled water pumps/heating water pumps.
  • With a global consideration of HVAC system status, AI-EOS will select optimal set points.
Trial Application and Expected Outcome
  • AI-EOS will use Artificial Neural Network (“ANN”) technique to model the HVAC components and their interrelationships based on historical data.
  • AI-EOS will also use Particle Swarm Optimization (“PSO”) technique to determine the optimal settings of the components based on the cooling loads required and the ambient conditions at that moment.
  • AI-EOS will automatically look at the opportunities in the building and make the necessary adjustments. By ANN and PSO, AI-EOS attempt to continuously search for opportunities to convert them into successful energy saving outcomes.
  • A 2-week evaluation is proposed for determination the AI-EOS performance. During the evaluation period, the AI-EOS operation will be taken place by the first 7 days and the next 7 days will be using CCMS operation mode.
  • During the evaluation period, all required logging data will be extracted from the CCMS and BEM accordingly. The evaluation will based on the extracted data and determine the performance of the AI-EOS.
Additional Solution Information EOS_2019_Catalogue_compressed.pdf
Info on I&T Solution Provider
Solution Provider:REC Green Technologies Company Limited
Address:Units A-D, 15/F., Goodman Kwai Chung, Logistics Centre, 585-609 Castle Peak Road, Kwai Chung, N.T.
Contact Person:Mr. Alvin Lai Kam Wing
Position:Manager
Tel: 26198820
Email: kwlai@rec-eng.com
Webpage: www.rec-gt.com

If you have interest in trial application of the I&T solutions, please click HERE.