State transition probability for the Markov Model dealing with on/off cooling schedule in dwellings

• Published in: Energy and buildings Vol. 37; no. 3; pp. 181 - 187
• Main Authors: Tanimoto, Jun, Hagishima, Aya
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2005; Elsevier
• Subjects: Applied sciences; Building technical equipments; Buildings; Buildings. Public works; Environmental engineering; Exact sciences and technology; Field measurement; Markov Model; On/off control for air conditioning system; State transition probability; Ventilation. Air conditioning; Field measurement; On/off control for air conditioning system; Markov Model; State transition probability; Air conditioning; Relative humidity; Cooling; Control system; Measurement result; Markov model; Dwelling building; Measurement in situ; Temperature measurement; Transition probability
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2004.02.002

We gathered field measurement data on five familial and three single dwellings during summer 2000 by deploying numerous handy type hygrothermal meters with self-recording functions to measure room air, globe and outdoor air temperatures. These measurements led to conclusions on the probability of turning on an air conditioning system versus indoor globe temperature and the ongoing probability of air conditioning versus outdoor temperature. This analysis was transformed into state transition probability functions, i.e. shifting from the off to on state and from the on to off state. Identifying these state transition probability functions is an important first step in applying the Markov Model to on/off state analysis for air conditioning systems, which is one of the significant approaches for dealing with the stochastic thermal load for HVAC system. The obtained state transition probability functions should help immeasurably in determining effective schedules for air conditioning operation from inhabitant occupancy schedules.