Simulating domestic hot water demand by means of a stochastic end-use model
Thesis (MEng)--Stellenbosch University, 2016.
ENGLISH ABSTRACT: The heating of domestic hot water (DHW) requires a substantial component of the energy demand in the residential sector, yet limited DHW demand information is available. An improved understanding of DHW demand has benefits for management of both energy and water demand and can lead to significant water and energy savings. However, understanding DHW demand is reasonably intricate, involving a spectrum of users and end-uses with varying event volumes, flow rates, timings of use and temperatures. In order to understand DHW demand, information is required on an end-use and temporal basis. Collecting data on DHW end-use consumption is expensive and involves complex field tests. A few previous studies were found that included comprehensive DHW consumption data, one of which was selected for use in this study. A stochastic model based on previous consumption data would be able to produce reliable DHW demand profiles. A stochastic end-use model was constructed in this study to derive diurnal DHW demand profiles for single family residences on a temporal scale of one minute. The model was designed to simulate diurnal DHW demand from a database of total water demand, which was available from an earlier international study. The model was able to convert total water demand to DHW demand using volume balances and various factors that influence DHW demand. An existing database was used to populate the model with probability distributions describing end-use characteristics. The model included five DHW end-uses in households. Each of the model iterations resulted in a diurnal demand profile with a hot water volume demand for each minute of the day. The profile is an aggregation of activated end-use events with stochastic frequencies, starting times and characteristics. The model applies a Monte Carlo method Stellenbosch University https://scholar.sun.ac.za iii to obtain average DHW demand profiles. Results are obtained after a finite number of iterations. Typical results obtained from the model for various scenarios are presented in the study. The study found that, as the number of occupants increased the DHW demand increased. The per capita DHW demand decreased logarithmically. Comparison with previous studies indicated that the model yields accurate results for DHW demand values with sensible diurnal demand profiles. Cyclic end-uses such as the dishwasher and washing machine were relatively complex to model. Furthermore, a sensitivity analysis revealed that the model result is most sensitive to the water heater temperature setting, with the cold water inlet temperature ranking second. Contrariwise, variables used to estimate heat loss from flow in pipes had an insignificant effect on total DHW demand. Various key results were found using the end-use model created in this study. With the dishwasher and washing machine end-uses connected to the water heater, average diurnal DHW demands were found to range between 259 ℓ/h/d in summer to 313 ℓ/h/d in winter. On the other hand, when the dishwasher and washing machine were not connected from the water heater, the demands ranged between 171 ℓ/h/d in summer to 202 ℓ/h/d in winter. Similarly, per capita DHW demand, with the dishwasher and washing machine end-uses connected to the water heater, ranged between 106 ℓ/c/d in summer and 127 ℓ/c/d in winter. Without the dishwasher and washing machine connected the per capita values ranged from 69 ℓ/c/d in summer to 81 ℓ/c/d in winter. The model in this study could also identify DHW on a per-end use basis.
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