Energy Performance Prediction and Validation in Green Buildings

Dear Colleagues,

The role of the building sector in combating climate change issues has increasingly gained the attention of the scientific community towards constructing green buildings as an effective measure to reduce building impacts, with significant benefits to society and the economy. 

Overall, the term “green building” lacks a clear and unequivocal definition, moving from narrower definitions (referring to purely environmental sustainability) to broader definitions (including economic and social aspects).  In the existing literature, the dominant approach to analyse green buildings involves comparing with a traditional building, highlighting the benefits from an environmental perspective (in terms of improved energy efficiency), an economic perspective (in terms of economic savings associated with improved performance), and a human perspective (in terms of improved indoor environmental conditions). Undoubtedly, building energy efficiency is one of the crucial issues addressed in green building research, although closely related to the other aspects. However, technological advancements provide manifold technologies that can be implemented in green buildings, whose study is becoming increasingly complex, requiring the implementation of proper procedures to study their effectiveness and thus adequately predict their performance. Moreover, the unavoidable changes in climatic conditions could also be explored, as the resulting changes in the building operational boundary conditions may affect their energy behaviour. Finally, validating the actual performance of green buildings still appears to be difficult to address, posing a significant obstacle to our understanding of their actual operational behaviour, and thus the actual benefits that these types of buildings provide.

This Special Issue intends to cover a broad range of research topics related to energy performance and validation. It aims to present recent research that addresses key questions, academic and industrial challenges, and comprehensive solutions, ultimately aimed at improving energy efficiency measures in green buildings.

Potential topics for this Special Issue include, but are not limited to, the following:

• Green building design;
• Digitalization for green buildings;
• Building performance simulation and energy efficiency;
• Thermal comfort in green buildings;
• Green building certifications;
• Life cycle analysis or life cycle cost analysis for green buildings;
• Innovative technologies and materials for green buildings;
• Methodologies and tools for performance prediction;
• Methodologies and tools for model validation;
• Renewable systems integration;
• Green buildings in changing climates.

Prof. Dr. Francesco Fiorito
Dr. Francesco Carlucci
Dr. Ludovica Campagna
Guest Editors

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• green buildings
• zero-carbon buildings
• energy efficiency
• thermal comfort
• sustainability
• building performance simulation
• climate change

Title: ML-Enabled Solar PV Electricity Generation Projection for Large University Campus Buildings to Reduce Onsite CO2 Emissions
Authors: Sahar Zargarzadeh, Aditya Ramnarayan, Felipe de Castro, Michael Ohadi
Affiliation: Smart and Small Thermal Systems (S2TS) Laboratory, Department of Mechanical Engineering, University of Maryland-College Park
Abstract: Mitigating CO2 emissions is crucial in reducing climate change, as these emissions contribute to global warming and its adverse effects on ecosystems. The primary utility data source is from the University of Maryland's campus; with over half of the campus's energy consumption being derived from electricity, reducing electricity consumption to mitigate carbon emissions is paramount. According to statistics, photovoltaic electricity is 15 times less carbon-intensive than natural gas and 30 times less than coal, making solar PV stand out among various methods of reducing electricity demand. This study aims to predict the impact of onsite solar photovoltaic-generated electricity on reducing CO2 emissions. 153 buildings on the campus were investigated, spanning the years 2015-2023, focusing on four key phases. In the first phase, PVWatts was used to gather data to predict PV-generated energy. This served as the foundation for phase two, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. Moreover, the SHAP technique was incorporated into the proposed framework to enhance model explainability. Phase three involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in the final phase to project future CO2 emissions post-solar PV installation. This comparison allowed the estimation of the potential reduction in emissions and the assessment of the progress toward Maryland’s goal of achieving net-zero emissions. This study's findings suggest that solar PV implementation could reduce the campus footprint by around 18% for the studied clusters of buildings, aligning with sustainability objectives and promoting cleaner energy use.

Title: Strategic Pathways to Sustainability: How One-Stop Shop and Turnkey Contract Business Models Drive the Adoption of Sustainable Technologies
Authors: Edda Donati
Affiliation: Dipartimento di Culture del Progetto, University IUAV of Venice

Title: Simulation of responsive envelopes in current and future climate scenarios: a new interactive computational platform for energy analyses
Authors: Francesco Carlucci; Francesco Fiorito
Affiliation: Polytechnic University of Bari
Abstract: Despite the strong interest concerning the responsive façades, today there are still few built examples and few tools to assess their benefits due to the complex description of the phenomenon. Hence, energy simulations should consider the interactions between a time-varying environment and an environment-dependent envelope, increasing the intricacy of the problem; moreover, these strong environment-envelope interlinkages, increase the importance of location and climate scenario considered. The aim of this study is providing a tool to easily model these phenomena in different geographical and climate contexts. For this purpose, an innovative Interactive Computational Platform (ICP) was developed based on EnergyPlus as simulation engine, Python as simulation manager, and Grasshopper as user interface. Thanks to a single user-friendly environment, the users can simply select the climate scenario, the location, the responsive technology, and its main properties to set and run the dynamic energy simulation. After an overview of the current state of the art, this study provides a description of the structure and workflow adopted for developing this platform and details regarding its functioning and inputs management. Finally, the platform was tested to run an evolutionary optimizations of an electrochromic window control strategy in different climate scenarios.