Experimental data of spatial-temporal event-driven modeling for occupant behavior studies using immersive virtual environments
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'It is widely accepted that the prediction of building energy performance is strongly related to the occupancy parameters. Currently, existing buildings and laboratories are the main sources for collecting occupancy related data. However, using such data for predicting the energy consumption of future buildings can create a con- siderable amount of uncertainties. Recent studies show that Immersive Virtual Environments (IVEs) have the potential to generate design and context sensitive occupant-related data. However, extended observations (longitudinal data covering relevant spatial and temporal events) which are necessary for developing quanti- tative predictive models are impractical using conventional IVEs. To that end, the authors propose a Spatial- Temporal Event-Driven (STED) modeling approach to enable IVEs for longitudinal studies.' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Contextual Variable","In-situ Hardware","Virtual Actuator","Virtual Human Building Interaction Experiment","Virtual Reality","Virtual Sensor","Virtual Setting"]} <|eot_id|>
 Here is the Description of a dataset. Description:'It is widely accepted that the prediction of building energy performance is strongly related to the occupancy parameters. Currently, existing buildings and laboratories are the main sources for collecting occupancy related data. However, using such data for predicting the energy consumption of future buildings can create a con- siderable amount of uncertainties. Recent studies show that Immersive Virtual Environments (IVEs) have the potential to generate design and context sensitive occupant-related data. However, extended observations (longitudinal data covering relevant spatial and temporal events) which are necessary for developing quanti- tative predictive models are impractical using conventional IVEs. To that end, the authors propose a Spatial- Temporal Event-Driven (STED) modeling approach to enable IVEs for longitudinal studies.' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
It is widely accepted that the prediction of building energy performance is strongly related to the occupancy parameters. Currently, existing buildings and laboratories are the main sources for collecting occupancy related data. However, using such data for predicting the energy consumption of future buildings can create a con- siderable amount of uncertainties. Recent studies show that Immersive Virtual Environments (IVEs) have the potential to generate design and context sensitive occupant-related data. However, extended observations (longitudinal data covering relevant spatial and temporal events) which are necessary for developing quanti- tative predictive models are impractical using conventional IVEs. To that end, the authors propose a Spatial- Temporal Event-Driven (STED) modeling approach to enable IVEs for longitudinal studies.
Contextual Variable; In-situ Hardware; Virtual Actuator; Virtual Human Building Interaction Experiment; Virtual Reality; Virtual Sensor; Virtual Setting