Case study of window opening behavior using field measurement results

Always cite the published version, so the author(s) will receive recognition through services that track citation counts, e.g. Scopus. If you need to cite the page number of the TSpace version (original manuscript or accepted manuscript) because you cannot access the published version, then cite the TSpace version in addition to the published version using the permanent URI (handle) found on the record page. Abstract:Air-source heat pumps (ASHPs) are commonly used in temperate climates throughout Europe and Asia to provide energy efficient heating and cooling. However, ASHPs have not been widely adopted for heating in colder climates because the coefficient of performance (COP) is lower when outdoor temperatures are colder. While many researchers are working on improving the cold-temperature performance of ASHPs by altering the design of the equipment, this work examines an innovative way to improve performance by operating an existing 'off-the-shelf' ASHP within a thermal buffer zone. This paper shows that operating an ASHP in a thermal buffer zone (TBZ) created by an enclosed balcony space can improve the COP in cold temperatures. An ASHP operating in a TBZ was tested in a climate chamber where the performance was monitored under a variety of climatic conditions. The temperature drawdown of the TBZ and the associated impact on the COP were observed. The TBZ solar heat gain rates required to improve the COP were identified for a range of exterior temperatures. Then, a suite-based energy model was developed and calibrated to simulate the performance of the ASHP operating in the TBZ. The model provided a reasonable prediction of the ASHP performance below 10 o C.

Section: Laboratory Apparatusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Testing and simulation of a low-temperature air-source heat pump operating in a thermal buffer zone

Touchie

Pressnail

2014

“…Kavgic et al [20] indicated that energy models often use standard air temperatures, such as the model default air temperatures used in the Traditional Approach, instead of air temperatures based on empirical data. However, interior air temperatures are not just the product of thermostat settings but they are also a 8 product of occupant behaviour as demonstrated by Jian et al [21] in their observation of the effect of window operation on suite temperature.…”

Section: Suite Conditions Comparisonmentioning

confidence: 99%

“…Window opening is an indication of occupants' dissatisfaction with their interior environment [21]. Through window displacement and temperature data, it was determined that residents of the subject building open their windows frequently, even during cold weather periods.…”

Section: Other Parametersmentioning

confidence: 99%

Using suite energy-use and interior condition data to improve energy modeling of a 1960s MURB

Touchie

Pressnail

2014

“…Building occupants' behaviour also significantly impacts the building energy performance. It is very difficult to deterministically represent their behaviour due to its temporal (usage of a specific appliance) or spatial (usage of a specific room) stochastic nature [8,9]. Wang et al [10] developed a model to simulate the occupant movement process based on the Markov chain.…”

Section: Introductionmentioning

confidence: 99%

Quantification of uncertainty in predicting building energy consumption: A stochastic approach

Brohus

Frier

Heiselberg

et al. 2012

Traditional building energy consumption calculation methods are characterised by rough approaches providing approximate figures with high and unknown levels of uncertainty. Lack of reliable energy resources and increasing concerns about climate change call for improved predictive tools.A new approach for the prediction of building energy consumption is presented. The approach quantifies the uncertainty of building energy consumption by means of stochastic differential equations. The approach is applied to a general heat balance for an arbitrary number of loads and zones in a building to determine the dynamic thermal response under random conditions. Two test cases are presented.The approach is found to work well, although computation time may be rather high. The results indicate that the impact of a stochastic description compared with a deterministic description may be modest for the dynamic thermal behaviour of buildings. However, for air flow and energy consumption it is found to be much more significant due to less "damping".Probabilistic methods establish a new approach to the prediction of building energy consumption, enabling designers to include stochastic parameters like inhabitant behaviour, operation, and maintenance to predict the performance of the systems and the level of certainty for fulfilling design requirements under random conditions.