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2000. Enhancing User Comfort in Smart Buildings through CO2 Forecasting and Operational Optimization
Invited abstract in session WB-53: Emissions and Heating Sector, stream Sustainable and Resilient Systems.
Wednesday, 10:30-12:00Room: 8007 (building: 202)
Authors (first author is the speaker)
| 1. | Albert Farriol Salas
|
| IREC | |
| 2. | Lucia Igualada
|
| Energy Systems Analytics Research Group, Intitut de Recerca en Energia de Catalunya | |
| 3. | Tomas Montes
|
| Energy Systems Analytics Research Group, Institut de Recerca en Energia de Catalunya | |
| 4. | Manel Serrano Borja
|
Abstract
This study introduces an optimal operational model for a smart building, showcasing the integration of indoor Carbon Dioxide (CO2) forecasting. The study presents an hourly operational model that encompasses the Heating, Ventilation, and Air conditioning (HVAC) system, electrical storage (ES), and Light-Emitting Diode (LED) lights, along with the utilization of photovoltaics (PV). The first objective is to employ a Long Short-Term Memory (LSTM) neural network to predict indoor CO2 concentrations and identify hours when these concentrations exceed the healthy threshold. Subsequently, the operational model is presented to ensure optimal energy usage, flexibility to selectively activate or deactivate the entire HVAC system during hours when forecasted CO2 levels surpass unhealthy thresholds. This proactive measure aims to prevent the escalation of indoor CO2 levels. By combining CO2 forecasting with operational optimization, this study contributes to enhancing the sustainability and performance of smart buildings while prioritizing the well-being of occupants. Additionally, the model ensures that optimal lighting levels are maintained at all times without compromising the visual comfort of building occupants. Theses algorithms and models have been developed in the context of the Netbuild project (CPP2021-009031), where the developments will be implemented in a real smart building located in Viladecans, Spain.
Keywords
- OR in Energy
- Scheduling
- Engineering Optimization
Status: accepted
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