SMART control algorithm for the HVAC system: thesis report

In industry, HVAC systems comprise slightly over 40% of the total electric usage of any given building or property type ( Department of Environment and Energy, 2013). With the constant demands we are placing on our environment it is evident we need to research ways to drive down this usage with a view to ultimately becoming a carbon neutral community. This thesis presents a new engineered self-learning control algorithm that can be used for all buildings to improve system performance to reduce overall energy usage, focusing on minimising peak electrical loads during the times that energy providers charge premium tariffs. The ultimate outcome is to minimise costs for the building through the manipulation of the HVAC system. This thesis focuses on practical ways that a portfolio or campus of buildings can use the indoor thermal characteristics of the building to reduce the peak electrical usage throughout the occupied day. Investigations were made around the benefits of integrating forecast weather data, indoor air requirements and electrical tariffs simultaneously within any given building to increase the efficiencies of the building (and thereby reduce the demands which relate to the overall operating costs of the building).