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1. Estimating Hourly Solar NIR Irradiance Using Meteorological Data for Sustainable Building Designand Engineeing

   https://doi.org/10.18086/solar.2020.01.12  

   Duan, Qiuhua; Feng, Yanxiao; Wang, Julian (July 2020, ASES Solar 2020 Proceedings)

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   Solar radiation is a key factor influencing sustainable building engineering, in terms of both optical and thermal properties of building envelopes. Solar irradiance data in a conventional weather data file are broadband, representing the total of ultraviolet (UV), visible light (VIS), and near-infrared radiation (NIR), three components of the solar spectrum; however, these three components play different roles in sustainable building design and engineering. For instance, solar VIS always provides benefits to indoor building energy savings (e.g., electrical lighting), while solar NIR is beneficial to building energy savings in winter but undesirable in summer. As a consequence, there is a need for reliable separate analyses focusing on individual solar radiation components. In this work, we explore and test classification-based modeling methods for decomposing hourly broadband global horizontal solar irradiance data in conventional weather files into hourly global horizontal solar NIR components. This model can then be conveniently implemented for sustainable building design and engineering purposes. 

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2. Random elastic space–time (REST) prediction

   https://doi.org/10.1016/j.spasta.2025.100904  

   Coloma, Nicolas; Kleiber, William (June 2025, Spatial Statistics)

   Statistical modeling and interpolation of space–time processes has gained increasing relevance over the last few years. However, real world data often exhibit characteristics that challenge conventional methods such as nonstationarity and temporal misalignment. For example, high frequency solar irradiance data are typically observed at fine temporal scales, but at sparse spatial sampling, so space–time interpolation is necessary to support solar energy studies. The nonstationarity and phase misalignment of such data challenges extant approaches. We propose random elastic space–time (REST) prediction, a novel method that addresses temporally-varying phase misalignment by combining elastic alignment and conventional kriging techniques. Moreover, uncertainty in both amplitude and phase alignment can be readily quantified in a conditional simulation framework, whereas conventional space–time methods only address am- plitude uncertainty. We illustrate our approach on a challenging solar irradiance dataset, where our method demonstrates superior predictive distributions compared to existing geostatistical and functional data analytic techniques. 

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3. Short-term solar irradiance forecasting under data transmission constraints

   https://doi.org/10.1016/j.renene.2024.121058  

   Hammond, Joshua E; Lara_Orozco, Ricardo A; Baldea, Michael; Korgel, Brian A (October 2024, Renewable Energy)

   We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model follows the convolutional neural network – long-short term memory architecture. Its inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The model focuses on predicting the deviation of irradiance from the persistence of cloudiness (POC) model. Inspired by control theory, a noise signal input is used to capture the presence of unknown and/or unmeasured input variables and is shown to improve model predictions, often considerably. Five years of data from the NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets and determine the best representations for time, the optimal span of input measurements, and the most impactful model input data (features). For the chosen validation data, the model achieves a mean absolute error of 74.29 W/m2 over a time horizon of up to two hours, compared to a baseline 134.35 W/m2 using the POC model. 

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4. Parametric Energy Simulation Methods for Solar-NIR Selective Glazing Systems

   https://doi.org/10.1088/1742-6596/2069/1/012129  

   Duan, Qiuhua; Zhang, Enhe; Hinkle, Laura; Wang, Julian (November 2021, Journal of Physics: Conference Series)

   Abstract Solar near-infrared (NIR) selective glazing systems have been proposed by incorporating photothermal effects (PTE) of a nanoparticle film into building windows. From an energy efficiency perspective, the nanoscale PTE forms unique inward-flowing heat by heating up the window interior surface temperature under solar near-infrared, significantly improving the window thermal performance. Also, the PTE-driven solar heat gains are dynamic upon solar radiation and weather conditions. However, the PTE on annual building energy use has not been investigated thoroughly, due to the lack of an accurate and appropriate energy simulation method. In this study, we used the EnergyPlus energy management system to develop a parametric energy model and simulation approach in which a solar-temperature-dependent thermal model was embedded into the parametric energy simulation workflow. Applying this method, we examined the solar near-infrared-dependent PTE-induced thermal performances of glazing systems and their effects on annual heating energy use in representative cold climates (i.e., Zones 4, 5, and 6). The results show that the dynamic model considering the PTE demonstrated more heating energy savings, up to 11.64% in cold climates, as opposed to the baseline model that ignored the PTE. This work presents a method to model and simulate the dynamic thermal performance of windows with PTE. 

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5. Co-Simulation and Validation of Automated Shading Devices using EnergyPlus and Radiance to optimize Building Energy Use

   Vanage, Soham; Kunwar, Niraj; Malekpour, Diba; Cetin, Kristen (January 2022, ASHRAE winter conference papers)

   Buildings in the U.S. are responsible for approximately 40% of energy and 70% of the electricity consumption. To address rising greenhouse gas emissions and climate changes, various studies have explored strategies to reduce energy consumption in buildings. One opportunity to improve the building envelope performance is through improvements to fenestrations, particularly complex multi-layer fenestration systems for exterior windows. Windows are the least thermally efficient of all components in a typical building envelope. Windows also permit solar radiation into a building, which significantly increases the building energy consumption during the summer season. Meanwhile, windows are necessary to provide occupants with natural light, a view to the outside, and to support productivity. Thus, there is a need to strike a balance between energy savings, and the thermal and visual comfort impacted by windows. Traditionally, shading devices are one method used to adjust the amount of heat and light entering an interior space. However, such shading devices are typically operated manually by occupants, and are seldom used effectively over time. Currently the building energy simulation program EnergyPlus, has limited capabilities to model shading devices, and more limited abilities to model dynamic fenestrations. In this study, thus, we propose to model and validate several types of automated multi-layer fenestration elements, using co-simulation of EnergyPlus and Radiance using laboratory-collected data. EnergyPlus was used to model energy consumption and thermal comfort while Radiance was used to model lighting levels. BCVTB was used to interface between EnergyPlus and Radiance to facilitate co-simulation. To validate the models, experimental data was collected from 5 illuminance sensors in an exterior office space located in a test facility in Ankeny, IA. This model methodology can be used to improve the flexibility and modeling capabilities of dynamic fenestration elements for building energy performance evaluation methods. 

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