Sviluppo e validazione di modelli energetici di edifici a partire dal monitoraggio dei consumi, per migliorare l’efficienza energetica, prevedere il carico e ottimizzare le utenze tecnologiche

Buildings are responsible for approximately 40% of energy consumption and 36% of CO2 emissions across the European Union (EU). Given these percentages, it’s easy to imagine that energy efficiency in buildings is a crucial topic, representing a key aspect in defining current energy policies at both national and community levels. In this context, the development of reliable energy models is a crucial resource within the activities of defining a Building Management System (BMS). A robust energy model can provide accurate estimates of consumption related to major utilities, such as Heating, Ventilating, and Air Conditioning (HVAC), which constitute a significant portion of total building energy consumption, especially in the tertiary sector. Additionally, technological, economic, and social transitions in recent years have had a significant impact on the energy landscape. Improvements in Information and Communication Technology (ICT), energy market liberalization, the emergence of active consumers and small local generation (turning consumers into “prosumers”), and increased environmental awareness have necessitated the development of new technologies and solutions for managing energy-intensive utilities. An essential aspect of utility management lies in the advanced features available to BMS, which can support the integration of unpredictable resources (such as renewable energy production), controllable utilities (such as HVAC systems), and the use of advanced Demand Response (DR) or Demand Side Management (DSM) algorithms. These techniques have become essential in addressing the challenges of managing multiple utilities, which can be complex and create issues related to safety, reliability, and power quality within an energy system. The presence of energy storage systems and new load types, such as electric vehicles (EVs), further complicates load absorption curves. In this context, simulation, forecasting, and automated decision-making tools derived from sophisticated data analytics algorithms are becoming crucial for designing, managing, and operating modern energy systems. The doctoral research discussed in this thesis, conducted at the IEES (Intelligent Electric Energy Systems) laboratory of the Department of Naval, Electrical, Electronic, and Telecommunication Engineering (DITEN) and funded by IESolutions – Intelligent Energy Solutions, primarily focused on studying and developing energy models for large tertiary sector buildings. The research also involved developing consumption prediction algorithms and applying them to real-world cases, with the goal of optimizing major energy systems, enhancing energy performance, and improving efficiency while maintaining user comfort. These activities were carried out within national and European research projects, alongside the development of computerized tools for monitoring energy consumption, potentially destined for technology transfer and commercialization. In the first chapter of this thesis, the application context in which algorithms and models were developed is analyzed, and the requirements are defined. The second chapter describes the data collection methodology from the field. Specifically, it discusses the hardware and software architecture used in an energy consumption monitoring system across various application domains, explaining the reasons for the chosen approach and the peculiarities of the considered tools. The third chapter defines the building modeling procedure used and provides a description of the software tools employed, along with their key features. Finally, an application case of an energy model for a building owned by the Department of Educational Sciences (DISFOR) at the University of Genoa is defined and described. In the fourth chapter, predictive algorithms are described, both concerning energy production and load curves. Different methodologies are evaluated for various scenarios, which may involve utilities in tertiary or residential buildings. The fifth chapter discusses the application of the developed prediction and diagnostic methodologies, both in large buildings and in new scenarios and contexts that emerged during the doctoral period. Examples include energy communities. Lastly, the thesis concludes with insights into research activities and potential developments, encompassing both research and technology transfer. Additionally, a list of projects undertaken during the doctoral years and scientific publications is presented.