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The concept of  environmental quality management  has been described in papers [1 - 4] that looked at the next generation of low energy buildings from the point of view of the occupant. Optimizing energy use is difficult for a few reasons: presence of dramatic changes in the manner we design and operate buildings, change in the role of an architect who must be a leader of interacting team, often quality management is biased towards the design more than on performance of the finished product and finally the need for integrated monitoring and modeling in the occupancy stage.  Effectively, we are integrating heating/cooling and ventilation with the structure at the same time as we verify the appropriateness of the new methods to evaluate performance of these systems. In this process we require double controls, one by the occupant and the other by the computerized (smart) control system. The traditional approaches to modify human behavior generally failed because occupants were not given enough control over their environment. Thus, a major part of the trend to a low-carbon, climate resilient future will be focused on methodology to include path from a complex field testing of building performance to simplified testing that combined with simple monitoring and data from utilities would allow assessment of the energy and carbon emission in a district of a city.  Our experience shows that preliminary design must be optimized during the period of service for all more complex buildings such as large residential, office or commercial buildings. In this context the artificial neural network approach appears to have significant advantages. Yet, traditionally ANN requires large data set to establish functional relations during the learning stage and therefore the first question is how precise can the control of temperature be when the heat exchanger is subjected to different climatic conditions.

Can artificial neuron networks be used for control of HVAC in environmental quality management systems?