Moreover, with the growing concerns of global warming and climate change, the accuracy of meteorological data used in building energy assessments has become increasingly critical.
Traditional methods for generating such data often fail to account for the complex interdependencies between meteorological variables, such as solar radiation, air temperature, and humidity.
These factors are vital for calculating energy usage and efficiency in buildings, yet existing models fall short in addressing their interconnected nature.
To overcome this limitation, a team of researchers from Osaka Metropolitan University, led by Associate Professor Jihui Yuan and Professor Emeritus Kazuo Emura, developed an innovative evaluation method.
The research, which was published in Scientific Reports, introduces a statistical model designed to represent the interdependence of multiple meteorological factors.
This new approach allows for the generation of probabilistic meteorological data, offering a more accurate and reliable basis for energy simulations in buildings.
Innovative research offers promising solutions for energy-efficient designs responding to unpredictable weather patterns.The team focused on modeling the temperature, solar radiation, and humidity at noon each day, then expanded this data to create a year’s worth of meteorological information, covering all 24 hours of each day.
This method’s strength lies in its ability to simulate the real-world relationships between these variables, thus improving the accuracy of building energy simulations.
The generated data was nearly identical to the original dataset, demonstrating the model’s precision and reliability.
Professor Yuan emphasized the importance of this breakthrough, stating, “We hope this method will lead to the promotion of energy-efficient building design that can respond to various weather conditions.”
The successful development of this method marks a significant step forward in improving energy efficiency in buildings, particularly in the face of unpredictable and changing weather patterns.