Hierarchical Modelling for CO2 Variation Prediction for HVAC System Operation
نویسندگان
چکیده
Residential and industrial buildings are significant consumers of energy, which can be reduced by controlling their respective Heating, Ventilation, Air Conditioning (HVAC) systems. Demand-based Ventilation (DCV) determines the operational times ventilation systems that depend on indoor air quality (IAQ) conditions, including CO2 concentration changes, occupants’ comfort requirements. The prediction changes act as a proxy estimator occupancy provide feedback about utility current controls. This paper proposes Hierarchical Model for Variation Predictions (HMCOVP) to accurately predict these variations. proposed framework addresses two concerns in state-of-the-art implementations. First, hierarchical structure enables fine-tuning produced models, facilitating transferability different spatial settings. Second, formulation incorporates time dependencies, defining relationship between IAQ factors. Toward goal, HMCOVP decouples variation into complementary steps. first step transforms lagged versions environmental features image representations variations’ direction. second combines step’s result with environment-specific historical data Through HMCOVP, predictions, outperformed approaches, help decision-making processes, reducing energy consumption carbon-based emissions.
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ژورنال
عنوان ژورنال: Algorithms
سال: 2023
ISSN: ['1999-4893']
DOI: https://doi.org/10.3390/a16050256