Abstract
BackgroundIn recent years, the monitoring of occupant presence patterns has become an imperative for building energy optimization. Very often, there is a significant discrepancy between the building energy performance predicted at the design stage and the actual performance rendered during the building operation. This stems from the difference in user occupancy. In spite of this, user interaction and feedback are rarely taken into account and evidence of the impact of occupant presence patterns on energy consumption is still scarce. Thus, the purpose of this study is to apply crowd-sensing techniques to understand how energy is consumed and how appropriate performance indicators should be defined to provide inputs for building operations regarding more efficient use of resources.MethodsMonitoring strategies were implemented in an office lab with controlled variables to collect quantitative data on occupancy patterns, ambient factors and energy consumption. In addition, crowd-sensing techniques were applied to model user activity in different ambient conditions over time and to contrast their occupancy with energy consumption patterns in combination with new inquiry tools to identify how occupants perceive their comfort level. In addition, a set of energy efficiency indicators was used to compare energy performance over different periods.ResultsIt was discovered that there is a strong relation between user occupancy patterns and energy consumption. However, more than 50% of energy was consumed when no user activity was registered. Energy performance indicators revealed that measuring energy efficiency in terms of kWh per surface area encourages a less efficient use of space and, therefore, including a coefficient of person hours is advisable. It was also discovered that users do not fully rely on feedback mechanisms and they prefer to take action to adapt the ambient conditions rather than simply expressing their opinion. Analysis of energy usage during the Covid-19 lock down revealed substantial use of energy contrary to what was expected. This was because home computers were used as terminals only, while the actual tasks were performed on the lab computers, using remote desktop connections, which were turned on 24/7. In addition, energy consumed by each employee at his/her home should be taken into account. Moreover, a set of practical recommendations was formulated.
Highlights
In recent years, the monitoring of occupant presence patterns has become an imperative for building energy optimization
In recent years, monitoring of user occupancy has become an imperative for building energy1 optimization, as it was realized that buildings do not consume energy by themselves but rather it is their occupants who create the energy demand and expect it to be satisfied
It is widely accepted that there is very often a significant discrepancy between building energy performance predicted at the design stage and the actual performance delivered when the building is in operation due to the difference in user occupancy [1–3]
Summary
The monitoring of occupant presence patterns has become an imperative for building energy optimization. There is a significant discrepancy between the building energy performance predicted at the design stage and the actual performance rendered during the building operation This stems from the difference in user occupancy. It is widely accepted that there is very often a significant discrepancy between building energy performance predicted at the design stage and the actual performance delivered when the building is in operation due to the difference in user occupancy [1–3]. Changes in building experience over time are refurbished or repurposed, used in different ways or host completely different activities than those they were designed for These circumstances have direct implications on building performance and demand new strategies for better user occupancy and energy demand sensing to perform in an optimal way
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