Generation of accurate weather files using a hybrid machine learning methodology for design and analysis of sustainable and resilient buildings

• A hybrid machine learning methodology is proposed for future weather prediction.
• The methodology is validated using real solar irradiance data.
• The importance of accurate future weather prediction is discussed.
• The characteristics and limitations of current weather files are reviewed.

Accurate weather information is required to assess the performance of sustainable buildings. Currently such performance is assessed using Typical Meteorological Year (TMY) weather files. TMY3 is the latest TMY file introduced by Department of Energy (DOE) and it represents the historical 30-year average of weather data more closely than any other available datasets. However, TMY3 data reflects the past and do not accurately forecast weather information thus leading to erroneous estimation of performance and economic feasibility. Therefore, more accurate methods are necessary to generate weather files so as to design buildings for sustainability and resilience. This article proposes an effective hybrid modeling methodology based on support vector machine regression and probability estimation to predict long term future weather variables. The methodology has been validated by using a dataset containing historical solar irradiance data for 54 years (1961–2014) for Cincinnati, Ohio. By implementing the proposed technique multiple models were trained with different segments of the dataset, which were used to predict the hourly solar irradiance for 8, 9, 10 and 11 years respectively. Subsequently, the predicted data and the current TMY3 data was compared with the actual historical data. The average increase in accuracy of the predicted over TMY3 direct normal and diffuse horizontal components of solar irradiance was found to be 38.43% and 11.8%. These findings suggest that the long term accuracy of the proposed modeling technique is greater than the current state-of-the-art.