• Data driven optimization
• Passive solar design
• Photovoltaic systems
• Solar systems

Since it arrived in the caves thousands of years ago, man has looked at energy to set proper living conditions. Originally, men relied on nature: fire, wind, and sun were the only sources to provide the energy requested. Passive design sinks its roots in the attempt to maximize available resources, by optimizing urban layouts, building shape and orientation, materials, and developing techniques to further exploit, store and distribute natural energy flows. With technological advancement, attention to natural elements has been overtaken by new systems based on new energy vectors, fossil fuel, first, and electricity later. The growing number of essential services, due to a growing demand for
comfort, have considerably pushed forward energy consumption, recklessly and without control, the environmental impact is the main consequence, later noted. To reduce the carbon footprint related to the civil sector, and save the planet, the pursuit of sustainable solutions, as to preserve the resources available for future generations, gave a decisive boost to the development and dissemination of renewable energy systems. In the civil sector, this mainly translates into
photovoltaic systems, whose diffusion has been driven by the electrification of building loads, the decreasing technology cost, and intrinsic characteristics such as ease of installation. This kind of
technology is particularly suitable as it is locally related to energy demand, as the system, and its production, are directly located where the consumption occurs: as a result, energy losses are reduced
and direct utilization of energy is promoted. These achievements come at a price, menaces that prevent a deeper integration of photovoltaic technology in the energy pie of the building sector.
Two kinds of challenges, systemic and technical, threaten the rational use of photovoltaic energy production. The systemic issue is related to the intermittent nature of the source; the technical limits lie in both architectural constraints for building integrated photovoltaic systems, and the not dispatchable nature of the system. In conclusion, the challenges are related to system design: the effectiveness of this kind of renewable energy system depends on time matching between consumption and production, which is directly affected by system size and the design of auxiliaries. The thesis goal is to deliver optimized scenarios for passive and active solar energy exploitation. The purpose is to determine sizing criteria for professionals, practitioners, and designers, to promote a rational development and diffusion of passive solar design and photovoltaic systems. The final scope is to provide general guidelines for both passive solar solutions integration in different kinds of buildings, and photovoltaic and electrical storage size, according to the different objective functions, from the definition of self-consumption schemes to reduce the pressure on the national electrical
grid and maximize the direct use of energy produced, to energy sharing configurations, to enhance a deeper penetration of renewable energy systems at urban scale at high efficacy. The novelty of the
thesis lies both in its scope and approach, a bottom-up methodology based on monitored data under real operating conditions, supported by simulated scenarios. The methodology is based on two different approaches, data-driven and simulation-based, over three main correlated topics. The path of the thesis followed a rising path, moving from the analysis of solar radiation to passive and active solar systems on a single-building scale, and finally to aggregated buildings and district-level energy models. The methodology mostly relied on monitored data, through collected data on solar irradiation, photovoltaic production, and building electricity consumption. Based on these data, in a bottom-up approach calibrated models have been developed and analyzed
in a single and aggregated configuration. In the absence of a direct source of data, passive solar design and district cluster final analysis is essentially based on energy simulation results. Firstly, the energy source, solar radiation, has been investigated, through the analysis of locally monitored time series. The investigation concerned the evaluation of the intermittent nature of solar radiation and the reliability of nominal, average data, commonly used in the pre-sizing methodology of solar-based devices. Data have been clustered according to different day types based on daily average values, and compared to monthly evaluation to determine the reliability of standard monthly solar
irradiation values, according to the established uncertainty criteria and mean relative and absolute error and relative root mean square error. Time series clustering techniques, based on the dynamic time warping method, and K-Means, have been applied to 5-minute monitored yearly data to detect potential clusters and the minimum number of referenced day types and series to effectively represent an annual trend of solar irradiation. As a first design step, the performance of passive solar configurations has been investigated over residential and office buildings. Once the effectiveness of passive solar design, from simple to
complex configurations, has been determined, a wide sensitivity analysis has been run to determine design guidelines and the impact of design parameters on the final results. Variables of the general investigation over five different passive schemes include orientation, thermal properties of the construction involved, and optic characteristics of the glazed surfaces. Simulation results have been compared to plot trends and behaviors that suggest the best configurations in different building scenarios. Finally, a methodology to exploit the achieved has been outlined over two test cases, residential and office buildings, analyzing the energy performance, to maximize, and the impact over
the building design and potential costs of implementation, to minimize. The integration of a transdisciplinary approach, analyzing the impact of an adaptive comfort model, showed the importance of a combined point of view, due to different elements interacting in synergy in the definition of the final energy performance. As a second step, the focus moved to active photovoltaic systems. Data have been collected from monitoring activity of different kinds of buildings, including traditional homes, residential nZEB, schools, and food stores. Through a bottom-up approach, monitored data have been analyzed to determine current system behavior. Calibrated energy models have been developed according to collected data to define optimal configuration, in terms of photovoltaic system size, based on different objective functions. Trends have been detected in the different types of buildings. The case
study of food stores has been more carefully analyzed, being a mainly electrical-fueled kind of building with high and constant loads: different scenarios have been investigated to use the energy locally produced, including building-to-vehicle strategies. Finally, the last scenario included building cluster analysis. The assumption, moving from an individual
building to cluster and district level, was to exploit potential complementary loads, to achieve a better production-to-load and grid interaction performance of the photovoltaic system and reduce the relative size. Firstly, the calibrated models based on monitored data have been used to investigate potential effective clusters based on the building aggregated energy demand. Full self-consumption and net-zero-energy clusters have been pursued, through the combination of high energy-intensive buildings, as food stores, as prosumers, and low energy-intensive buildings, as homes, as main consumers. Lastly,
a methodology, based on two developed key performance indicators is proposed to determine optimal building clusters within urban districts, with an assumption of searching for local clusters that can rely
on a dedicated electrical grid. The methodology is then tested on simulation-based results of an optimal district in the case study of the city of Fribourg, Switzerland. A multi-objective analysis, with
techno-economic indicators, has been run to determine the results achievable in terms of energy performance and saving of PV power installed. Results highlighted the impact of solar radiation clustering to represent and summarize yearly trends for sizing applications, with summer showing high reliability. A minimum set of four-day types can
properly represent the overall annual dataset. The contribution of passive solar design can achieve 10-15% in total energy savings even in existing buildings, up to 65% including adaptive comfort models. The optimization process on the photovoltaic system determine consistent sizing criteria among analyzed buildings, while district-level analysis showed how either full self-consumption or net zero energy cluster can be achieved and identified in a city.