Model predictive energy control of ventilation for underground stations

Smart building systems are opening up new markets, nevertheless the implementation of these novel technologies still lacks suitable and proven whole engineering solutions in complex buildings. This paper presents a detailed approach for the ventilation control of an underground space, as an example of application of the developed solution to a very harsh environment but also highly demanding in terms of energy consumption. The underground spaces are characterized by a particular thermal behavior, because of the continuous and huge thermal exchange they have with the outside, via the openings and the ground surrounding the majority of the building. The main objective of the developed methodology is to reduce energy consumption of ventilation control while maintaining acceptable comfort levels: succeeding in achieving this twofold goal in a real station and the generalization of the approach are the most relevant contributions of the paper. The developed solution is based on a Model-based Predictive Control algorithm used together with a proper monitoring platform. The model predictive control is based on a Bayesian environmental prediction model, which works in cooperation with a weather forecast web service, schedule-based predictions about trains and external fans and an occupancy detection system to appraise the real amount of people. The prediction model develops scenarios useful to allow the controller acting in advance in order to adapt the system to the current and future conditions of use, taking profit of the knowledge of the real ventilation demand. Finally, the proposed control architecture is applied to the Passeig de Gràcia metro station in Barcelona as a case study, validating the usefulness of the proposed approach and obtaining more than 30% of energy savings in the ventilation system, while maintaining the pre-existing comfort levels. The saving percentage values estimated by simulation are confirmed by the direct measures continuously taken on site through energy-meters

Management of Unexpected Events in Emergency Scenarios

Il tradizionale approccio alla gestione delle emergenze negli edifici si basa sulla previsione deterministica dei principali scenari che potrebbero verificarsi, non tenendo conto degli eventi casuali e inaspettati che potrebbero presentarsi influendo negativamente sulle operazioni da eseguire in caso di emergenza. L’approccio attuale presenta diversi punti di debolezza dovuti ad un’acquisizione di dati sullo stato di emergenza scarsa e poco efficiente e a diversi “colli di bottiglia” nel processo decisionale, che appare irrigidito dal suo carattere eccessivamente gerarchico. Il contributo scientifico di questa tesi, consiste nello sviluppo di una metodologia nella gestione delle emergenze che presenta innovative caratteristiche di efficienza in tempo reale, resilienza e capacità di risoluzione di problemi in modi non convenzionali. Si propone un cambio di rotta da un approccio deterministico ad uno volto ad affrontare la contingenza delle situazioni che potrebbero verificarsi, elevando la flessibilità e adattabilità del sistema, attraverso l’applicazione della teoria “olonica”, la quale promuove maggiore autonomia e cooperazione tra i livelli più bassi della gerarchia in risposta a un workflow troppo rigido. La ricerca ha condotto alla definizione di un’architettura di sistema a supporto delle operazioni standard previste da normativa, rendendole più efficienti attraverso l’utilizzo di dati aggiornati ed eterogenei, proponendo soluzioni alternative in caso di imprevisti, rapidamente calcolate. La metodologia è stata implementata in un caso studio, dettagliandone l’architettura di sistema fondata sull’utilizzo di modelli BIM come “contenitori” di informazioni aggiornate, coerenti e complete sull’edificio, di Reti Bayesiane per selezionare le azioni alternative più promettenti analizzando rapidamente le serie di dati al momento disponibili e una piattaforma di Realtà Virtuale come collettore di dati provenienti da fonti eterogenee e ambiente di simulazione con elementi di Intelligenza Artificiale.The traditional approach to the building emergency management is based on a deterministic prevision of the main scenarios, regardless of contextual, changing and unexpected events that may happen and seriously affect the effectiveness of emergency measures. The current approach results affected by several weaknesses due to a poor and inefficient data acquisition regarding the evolving scenario and to the bottlenecks in the decision flow, deriving from a too rigid hierarchical workflow. The contribution of this dissertation lies on the development of a new methodology in the emergency management based on the principles of real-time effectiveness, resilience and unconventional problem solving. A shift from a deterministic to a contingent approach is proposed, leveraging the system’s flexibility and adaptability to changing scenarios, founded on the application of the Holonic Theory to the emergency management. This theory promotes a higher autonomy and cooperation among the actors of the lowest level of the hierarchy, as a response to a too rigid hierarchical workflow, often affected by bottlenecks in the decision flow that may result fatal in critical scenarios like the emergency ones. The research has conducted to the definition of a system architecture as support to the standard rescue operations, which improves the usual approach supplying more updated and significant information from different sources and investigating unusual solutions for rescue purposes in case of unforeseen events. It relies on the means of BIM (Building Information Modelling), as comprehensive building information provider, Bayesian Networks to make the decision flow more flexible and able to cope with uncertainties and Virtual Reality engines to collect data from heterogeneous sources and test the overall system. The bottlenecks in the process flow result considerably reduced, providing the system with a faster capability to face unexpected events, endowing it with the required resilience and adaptability