Data science and Information Technologies are currently playing a crucial role in the context of buildings sustainability and energy efficiency. The achievements of relevant energy performances through the optimal control of building subsystems and the introduction of innovative decision support tools in the early-stages of the design are noteworthy features of contemporary smart buildings. Smart buildings are demonstrating several distinctive features that are opening new markets and establishing new innovative design solution. Not surprisingly, the design phase of new buildings has acquired primary importance for the improvement of sustainability and the reduction of the energy demand. Nowadays, buildings represent 40% of world primary energy consumption and 24% of greenhouse emissions. There is a growing interest in precisely understanding and profiling the actual building energy consumptions (e.g., when higher peaks occur and how much they are). This implies that the development of advanced energy consumption measurements, verification instruments and forecasting algorithms is an emerging need. Indeed, existing building simulation tools provide a realistic representation of building operations only when the simulated models are properly calibrated and validated. Thanks to these instruments, in this context, the objective of the present research thesis is to carry out a development of decision support tool, based on a parametric analysis, for helping designers in evaluating the choice of different building components, such as insulation foams and glazing systems and evaluating their benefits for thermal comfort and energy savings through a real building performance simulation model. In fact, the developed tool is intended to play an important role in the early design phase, when it is well known that parametric analysis is useful for evaluating high-level benchmarking. On the other hand, the complexity related to the large number of variables affecting the building behavior prevents achieving a precise picture of the real-world building operation. To address such an issue, the proposed method enables powerful parametric studies in a reasonable time. The main feature of the proposed tool is the integration of the definition of the building model including the calibration and validation procedures, with a sensitivity analyzer based on an automatic process. The proposed tool compares several possible alternative models of the given building, obtained by automatically combining different thermal behaviors, physical parameters and climatic zones, and using long-term comfort indices. The main goal is to estimate e the values of long-term indices for a preliminary thermal performance evaluation of the given building in different contexts and with different parameters combinations. In particular, a Matlab tested has been developed for co-simulation with the whole-building energy simulator EnergyPlus. The tool is provided with a front-end in order to facilitate the configuration process in defining the building model and listing the associated building parameters needed for the co-simulation. Moreover, the front-end allows the selection of desired performance indices: for instance, the Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) indices are implemented and standardized in accordance with the ASHRAE 55 Thermal Comfort Code. Finally, the tool is provided with a data analysis module: after co-simulation runs, the output data from EnergyPlus are aggregated, analyzed and visualized in Matlab both in tabular and graphical views. As a result, this analysis can be useful in several ways including: monitoring of the building thermal comfort, controlling the efficiency of the HVAC equipment, estimating the energy demand by utility companies and forecasting the energy savings due to equipment retrofits or implementation of an energy conservation measure. Furthermore, this type of approach is also used as an inverse modeling tool to better understand the performance requirements of materials, for example, to establish the preferred physical property ranges that lead to zero-energy building enclosures. The first part of the thesis focuses on a literature review and the state-of-art of the building performance simulation, calibration and validation approaches and optimization techniques for buildings' thermal comfort and energy savings. While, the second part of the thesis focuses on a real case study (Solatrium House), where is applied a novel calibration and validation approach and the description of the conducted parametric analysis for evaluating the benefits of different design choices on a building's performance.
|Titolo:||An innovative decision-making approach for a sustainable building design|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||5.14 Tesi di dottorato|