Abstract
Heating, ventilation and air conditioning systems represent considerable potential for energy savings, which can be realized through intelligent occupancy-centered control strategies. In this work, both supervised and unsupervised algorithms to forecast occupancy are proposed with the highest accuracies of 98.3% and 97.6%, respectively. Building on their output, a rule-based air conditioning scheduling technique is developed. As an example, a potential of 15.4% of energy savings is calculated using a dataset collected in a mid-size (4000 m2) building in Portugal.
Highlights
Management of the energy demand has become an increasingly popular topic of research over the last decades in response to climate change
This paper aims to address the highlighted drawbacks in the occupancy forecasting using a context-based approach and to demonstrate the potential for energy savings in buildings through occupancybased HVAC control
We have proposed the rule-based HVAC scheduling technique using occupancy forecasting
Summary
Management of the energy demand has become an increasingly popular topic of research over the last decades in response to climate change. Energy use has grown across all sectors, including industry, buildings and transportation. Buildings alone account for 38% of the total final energy consumption in the EU and 40% in the US [1,2]. Within these shares, approximately 50% is consumed for heating, ventilation and air conditioning (HVAC), making it the largest energy user in both, tertiary and residential sectors. Buildings’ energy consumption is mainly influenced by several factors, such as their location, thermal properties, construction characteristics, occupants’ behavior and HVAC system quality. Minimizing buildings’ consumption can be done at the design stage through extensive energy simulations, the study [4] has shown that a significant gap between planned and actual energy use remains
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