In the current era of architecture, sustainability and energy efficiency are becoming increasingly important, while at the same time, advanced technological tools and analytical methods are reshaping the design and construction of buildings. Architects must think responsibly and globally, as buildings account for a significant proportion of the world's energy use. As architects, we have a responsibility to create ecologically optimal facilities for the long term. With this in mind, we would like to present applications that trace the chronological milestones in the development of energy analysis. This paper provides a detailed overview of the different methods of energy analysis. The methods include software developed specifically for energy analysis, an analysis add-on built into modeling software, and among the more innovative technologies, we have also examined parametric design and methodologies based on artificial intelligence algorithms. We have tried to select these methodologies and software in a diversified way to get a more comprehensive picture of how they work. The main aim of this paper is to compare the conclusions drawn from case studies of our previous energy research and from the studies of these energy software, partly subjectively and partly with an objective perspective that tightens subjectivity. As such, a set of criteria we have defined will guide the structure of this analysis. In this article, we will try to highlight the advantages and disadvantages of each method, and we will also try to consider the importance of 3D model-based analysis.

In an era where sustainability and energy efficiency are paramount in architecture, advanced technological tools, and analytical methodologies are restructuring the design and construction of buildings. Energy analysis methods, including parametric modeling and artificial intelligence, offer architects unprecedented capabilities to comprehensively assess and optimize energy performance throughout a building's lifecycle. This paper reviews a wide range of energy analysis methods, including dedicated software tools, in-built applications, parametric tools, and artificial intelligence. It highlights the benefits and limitations of each method, emphasizing model-based methodologies. Dedicated software simplifies energy studies but requires manual data input, limiting flexibility and scalability. In-built applications, such as ArchiCAD or Autodesk Revit, enable automatic energy analysis but rely on detailed models. Parametric tools like Rhinoceros-Grasshopper enable flexible design variations but demand specialized knowledge. Artificial intelligence-driven tools like CoveTool and Autodesk Forma leverage AI algorithms for rapid energy modeling but are still evolving. In this review article, we would like to highlight the importance of energy analysis in building design and the need for architects to learn about new technologies. All these are necessary for a sustainable future.

The energy performance of buildings has become increasingly important, driven by efficiency, awareness, and sustainability goals. While attention often focuses on passive or zero-emission buildings, upgrading the thermal insulation of existing stock remains essential. In Hungary, many buildings still fail to meet current standards, from 19th-century apartments in Budapest to post-war panel blocks designed without thermal considerations. This study examined mineral-based insulation boards made from autoclaved aerated concrete under controlled exposure scenarios: cold winter (−20 °C), humid spring/autumn (10 °C, 90% RH), and hot summer (70 °C, 20% RH). Standardized tests assessed mass variation, compressive strength, thermal conductivity, and short-term water absorption. Results show that high humidity caused up to 14% loss of compressive strength and a 17–18% increase in thermal conductivity after 14 days, severely reducing insulation capacity. In contrast, extreme cold and heat induced only minor changes. Moisture was identified as the most critical factor compromising both mechanical stability and energy efficiency. The findings provide reproducible evidence of environmental sensitivity and underline the importance of durability assessments in designing and retrofitting energy-efficient buildings.

AbstractPhotovoltaic systems are increasingly applied in buildings to reduce energy costs and support sustainability. Their performance depends on factors such as panel angle, orientation, and temperature, which significantly affect energy yield. This study compares the actual output of an operating photovoltaic system with results from three calculation methods: manual estimation, 3D modeling, and artificial intelligence-based evaluation. The photovoltaic geographical information system and Rhino-Grasshopper methods proved most accurate, deviating by less than 10% from measured data due to their use of extensive, long-term meteorological datasets that ensure reliable performance prediction.