An advanced control strategy for optimizing HVAC system performance in non-residential buildings
(English) This PhD research addresses the scientific and practical challenges in implementing advanced control systems (ACS) for HVAC optimization in non-residential buildings. While adaptive and predictive strategies, especially model predictive control (MPC) and neural network (NN)-based methods,...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/449282 |
| Acceso en línea: | https://hdl.handle.net/2117/449282 https://dx.doi.org/10.5821/dissertation-2117-449282 |
| Access Level: | acceso abierto |
| Palabra clave: | HVAC Systems Building Energy Efficiency Advanced Control Strategies Model Predictive Control (MPC) Energy Optimization Non-Residential Buildings Building Automation Systems Thermal Comfort 624 - Enginyeria civil i de la construcció en general 69 - Materials de construcció. Pràctiques i procediments de construcció 502 - Natura. Estudi, conservació i protecció de la natura Àrees temàtiques de la UPC::Enginyeria civil Àrees temàtiques de la UPC::Edificació Àrees temàtiques de la UPC::Desenvolupament humà i sostenible |
| Sumario: | (English) This PhD research addresses the scientific and practical challenges in implementing advanced control systems (ACS) for HVAC optimization in non-residential buildings. While adaptive and predictive strategies, especially model predictive control (MPC) and neural network (NN)-based methods, have shown promise in research, real-world deployment remains limited. Barriers include insufficient building historical data, technical limitations of HVAC systems, lack of building energy management systems (BEMS) standardization, and low institutional readiness. To address these, this thesis uses a dual-method approach combining empirical analysis and simulation-based experimentation. First, a survey of 676 non-residential buildings evaluates BEMS practices, focusing on HVAC control, data storage, and predictive control adoption. While smart metering and sensors are increasingly common, predictive control is reported in only 0.6% of buildings. Key barriers identified include a lack of environmental data logging, obsolete HVAC systems that do not support integration with predictive control technologies, limited technical expertise among building operators, and insufficient investment frameworks, particularly in the public sector. To respond to data and implementation challenges, the second part develops and evaluates NN-based predictive controllers using a calibrated building energy model. Eight NN models are trained on varying amounts of historical data to assess impacts on prediction accuracy and HVAC performance. Validations use consistent KPIs for thermal comfort and energy efficiency. Results show that 1–4 months of data are needed for acceptable performance, reaching a performance threshold after two years. Data preprocessing helps in data-limited cases (<100 instances), but adds little value with larger datasets, suggesting a context-specific role. Further analysis explores operational and climatic sensitivities. In cold climates and post-HVAC shutdown periods (e.g., Monday mornings), models struggle due to sparse training data. Performance improves mid-week and in warm zones, highlighting the need for diverse and climate-adapted training data. Compared to rule-based scheduling, NN controllers consistently improve energy use and comfort, especially when supported by adequate data and system configuration. This thesis offers novel insights into deploying intelligent HVAC control systems. It identifies data thresholds for effective predictive control, clarifies preprocessing roles, and provides guidance on model adaptation to climate and operations. It also highlights broader needs such as standardizing data acquisition, training energy professionals, and fostering public-private collaboration to reduce implementation risk. The findings support scalable predictive control in practice and contribute to the goals of energy efficiency, smart building management, and decarbonization. |
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