Reduce energy consumption of HVAC systems using Machine Learning
Updated: Nov 22, 2021
Conserve It's Vice President of Operations, Chirayu Shah presented 'Reduce Energy Consumption of your HVAC Systems Using Artificial Intelligence based Chiller Plant Control and Optimisation Systems' at BEX Asia 2021.
Buildings consume almost a quarter of Worlds Energy Consumption and hence are one of the major sources of Emissions globally. In commercial buildings, HVAC is by far the most energy intensive system, accounting for close to half of the total energy consumption. For this reason every efficiency improvement in HVAC performance can significantly reduce the energy profile of the building, turning HVAC optimisation into a value generating opportunity.
Recent years, we have witnessed a growing interest in efficient HVAC control and optimisation, but due to its complex structure, rigorous optimisation proves to be challenging to many.
Created with an in-depth understanding of all thermodynamic variables involved in managing chiller plant room HVAC equipment, PlantPRO enables optimum control of every device and its integration into a single synergistic system. This session will outline an artificial intelligence-supported model predictive controller (AI-MPC) to optimise the energy consumption in a buildings . AI-MPC uses a high-level model to generate predictions of system load and resources to minimise operation costs. AI methods continuously learn and update the system models based on feedback from measurements and generates predictions and defines optimal trajectories for HVAC controls without compromising on occupant comfort. This intelligent self generating system is able to verify its own performance and is versatile enough so that it can provide the relevant details to everyone ranging from Property Management companies, Facility Managers, Energy Managers, HVAC Mechanical Contractors and Controls Technicians and Contractors.