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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)

   null (Ed.)

   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. 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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3. 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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4. Solar-TK: A Data-Driven Toolkit for Solar PV Performance Modeling and Forecasting

   https://doi.org/10.1109/MASS.2019.00060  

   Bashir, Noman ; Chen, Dong ; Irwin, David ; Shenoy, Prashant ( November 2019 , IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS))

   Solar energy capacity is continuing to increase. The key challenge with integrating solar into buildings and the electric grid is its high power generation variability, which is a function of many factors, including a site's location, time, weather, and numerous physical attributes. There has been significant prior work on solar performance modeling and forecasting that infers a site's current and future solar generation based on these factors. Accurate solar performance models and forecasts are also a pre-requisite for conducting a wide range of building and grid energy-efficiency research. Unfortunately, much of the prior work is not accessible to researchers, either because it has not been released as open source, is time-consuming to re-implement, or requires access to proprietary data sources. To address the problem, we present Solar-TK, a data-driven toolkit for solar performance modeling and forecasting that is simple, extensible, and publicly accessible. Solar-TK's simple approach models and forecasts a site's solar output given only its location and a small amount of historical generation data. Solar-TK's extensible design includes a small collection of independent modules that connect together to implement basic modeling and forecasting, while also enabling users to implement new energy analytics. We plan to release Solar-TK as open source to enable research that requires realistic solar models and forecasts, and to serve as a baseline for comparing new solar modeling and forecasting techniques. We compare Solar-TK's simple approach with PVlib and show that it yields comparable accuracy. We present three case studies showing how Solar-TK can advance energy-efficiency research. 

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5. Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

   https://doi.org/10.3390/en12214055  

   Wojtkiewicz, Jessica ; Hosseini, Matin ; Gottumukkala, Raju ; Chambers, Terrence Lynn ( November 2019 , Energies)

   Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but is also characterized by a high degree of variability and uncertainty. Deep learning methods have the ability to learn long-term dependencies within sequential data. We investigate the application of Gated Recurrent Units (GRU) to forecast solar irradiance and present the results of applying multivariate GRU to forecast hourly solar irradiance in Phoenix, Arizona. We compare and evaluate the performance of GRU against Long Short-Term Memory (LSTM) using strictly historical solar irradiance data as well as the addition of exogenous weather variables and cloud cover data. Based on our results, we found that the addition of exogenous weather variables and cloud cover data in both GRU and LSTM significantly improved forecasting accuracy, performing better than univariate and statistical models. 

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