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

Tanimoto, Jun; Hagishima, Aya

doi:10.1016/j.enbuild.2004.02.002

Cited by 61 publications

(25 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A questionnaire survey with 554 responses on AC-unit usage during the sleeping hours in Hong Kong revealed that 83% of the occupants use their AC-unit for more than 5 h during the sleeping period [11], but this result cannot be applied directly to different weather conditions. Tanimoto and Hagishima [12] applied the Malkov model to relate AC usage to different time intervals of the day based on the data from eight observed dwellings. Nicol and Humphreys [13] presented a logit line for cooling in mixed mode office buildings.…”

Section: Introductionmentioning

confidence: 99%

Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions

Schweiker

Shukuya

2009

Building and Environment

145

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions

Schweiker

Shukuya

2009

Building and Environment

145

View full text Add to dashboard Cite

“…The authors have developed Total Utility Demand Prediction System (TUD-PS) as a novel framework for predicting high time-resolution utility demands in a dwelling, considering various stochastic processes, such as the inhabitants' behaviour schedule and meteorology. As reported in our previous studies (Tanimoto and Hagishima 2005, Tanimoto et al 2008a, TUD-PS can examine the building thermal system model and the stochastic inhabitant behaviour schedule model simultaneously in the form of a dynamic numerical prediction system for utility loads, such as thermal load, power, gas, water and hot water demand with a 15-min time resolution. By comparing field measurement datasets obtained from a couple of residential buildings, we have already validated how appropriately TUD-PS can reproduce the bottom-up demands (Tanimoto et al 2008a,b,c).…”

Section: Introductionmentioning

confidence: 99%

Total utility demand prediction for multi-dwelling sites by a bottom-up approach considering variations of inhabitants’ behaviour schedules

Tanimoto

Hagishima

Iwai

et al. 2013

Journal of Building Performance Simulation

Self Cite

View full text Add to dashboard Cite

This article reports systematic case studies based on a Total Utility Demand Prediction System presented in the authors' previous works, in which one can follow a bottom-up approach to accurately calculate the time series utility loads (energy, power, city water, hot water, etc.) for multi-dwelling systems, including residential buildings, residential block areas and even an entire city. This calculation considers the behavioural variations of the inhabitants of the dwellings. In the case studies, we assumed a residential building consisting of 100 independent dwellings to accurately predict various peak demands and seasonal or annual demands. A series of simulations reveals that considering time-varying inhabitant behaviour schedules significantly affects the peak loads. Hence, HVAC COP, inhabitants' age and their family type significantly influence the peak loads and their accurate timeseries.

show abstract

“…Therefore, we have been developing a novel framework for predicting high time resolution utility demands in a dwelling or aggregated dwellings, considering various stochastic processes such as the schedules of the inhabitants, meteorology, etc. Firstly, we developed a stochastic model to deal with the probabilistic events for turning the heating, ventilating and air conditioning (HVAC) on/off, based on the Markov Chain Theory (Tanimoto and Hagishima 2005). We also presented a time-varying raw data of the schedules of the inhabitants (15-min resolution), and a generating algorithm that utilizes only a statistical database available publicly (Tanimoto et al 2008a-c).…”

Section: Introductionmentioning

confidence: 99%

Total utility demand prediction system for dwellings based on stochastic processes of actual inhabitants

Tanimoto

Hagishima

2010

Journal of Building Performance Simulation

Self Cite

View full text Add to dashboard Cite

This article describes a new methodology to calculate the likely utility load profiles (energy such as power, natural gas, space heating and cooling, and other thermal requirements, as well as city water) in a dwelling. This calculation takes into account the behavioural variations of the dwelling inhabitants. The proposed method contains a procedure for cooling load calculations based on a series of Monte Carlo simulations where the heating, ventilating and air conditioning (HVAC) on/off state and the indoor heat generation schedules are varied, time-step by timestep. A data set of time-varying inhabitant behaviour schedules, with a 15-min resolution, generated by the authors in previous studies and validated by a comparison analysis to several field measurement data sets, was integrated into the model. The established model, which is called the total utility demand prediction system, can be applied to, for example, likely estimation of an integrated space maximum requirement, such as the total load of a building or an urban area. In a series of numerical experiments, huge discrepancies were found between the conventional results and those considering the time-varying inhabitant behaviour schedules. In particular, deriving the dynamic state change, of having the HVAC on/off from the inhabitants' schedules, was found to be a significant factor in the maximum cooling and heating loads.

show abstract

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

Cited by 61 publications

References 3 publications

Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions

Comparison of theoretical and statistical models of air-conditioning-unit usage behaviour in a residential setting under Japanese climatic conditions

Total utility demand prediction for multi-dwelling sites by a bottom-up approach considering variations of inhabitants’ behaviour schedules

Total utility demand prediction system for dwellings based on stochastic processes of actual inhabitants

Contact Info

Product

Resources

About