Machine learning methods for solar radiation forecasting: A review

[1] A new fully coupled meteorology-chemistry-aerosol model is used to simulate the urban- to regional-scale variations in trace gases, particulates, and aerosol direct radiative forcing in the vicinity of Houston over a 5 day summer period. Model performance is evaluated using a wide range of meteorological, chemistry, and particulate measurements obtained during the 2000 Texas Air Quality Study. The predicted trace gas and particulate distributions were qualitatively similar to the surface and aircraft measurements with considerable spatial variations resulting from urban, power plant, and industrial sources of primary pollutants. Sulfate, organic carbon, and other inorganics were the largest constituents of the predicted particulates. The predicted shortwave radiation was 30 to 40 W m−2 closer to the observations when the aerosol optical properties were incorporated into the shortwave radiation scheme; however, the predicted hourly aerosol radiative forcing was still underestimated by 10 to 50 W m−2. The predicted aerosol radiative forcing was larger over Houston and the industrial ship channel than over the rural areas, consistent with surface measurements. The differences between the observed and simulated aerosol radiative forcing resulted from transport errors, relative humidity errors in the upper convective boundary layer that affect aerosol water content, secondary organic aerosols that were not yet included in the model, and uncertainties in the primary particulate emission rates. The current model was run in a predictive mode and demonstrates the challenges of accurately simulating all of the meteorological, chemical, and aerosol parameters over urban to regional scales that can affect aerosol radiative forcing.

Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model

The sun's total and spectral irradiance for solar energy applications and solar radiation models

http://coimbra.ucsd.edu/publications/papers/2013_Inman_Pedro_Coimbra.pdf

Solar forecasting methods for renewable energy integration

To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.

Forecasting of photovoltaic power generation and model optimization: A review

http://www.asrc.cestm.albany.edu/perez/publications/Solar%20Resource%20Assessment%20and%20Modeling/Papers%20on%20Resource%20Assessment%20and%20Satellites/2009-validation-of-short-and-medium-term-forecasts.pdf

Validation of short and medium term operational solar radiation forecasts in the US

https://www.calsolarresearch.ca.gov/images/stories/documents/Sol1_funded_proj_docs/cpr_parameterization_of_short_term_variability-11-4-10.pdf

Parameterization of site-specific short-term irradiance variability

The U.S. Department of Energy funded the development of the multifilter rotating shadowband radiometer (MFRSR) as part of the Atmospheric Radiation Measurement (ARM) program. This seven-channel radiometer began operation at the first ARM site in 1992 and at the Department of Energy Quantitative Links (QL) sites in the fall of 1991; three of the QL sites continue to operate, although this program was discontinued after 1995. This paper describes the use of the MFRSR in acquiring aerosol optical depth data, including the in-field calibration procedure and a partial validation of this process. Multiyear measurements of aerosol optical depth from three of the sites indicate similar phasing of seasonal and interannual changes, but with notable differences in the magnitude of the aerosol optical depth. Published papers that use these aerosol data are highlighted, and public access to these and future data sets for scientific studies are explained.

Multiyear measurements of aerosol optical depth in the Atmospheric Radiation Measurement and Quantitative Links programs

[1] Aerosol optical depth (AOD) has been measured at the Atmospheric Radiation Measurement Program central facility near Lamont, Oklahoma, since the fall of 1992. Most of the data presented are from the multifilter rotating shadowband radiometer, a narrow‐band, interference‐filter Sun radiometer with five aerosol bands in the visible and near infrared; however, AOD measurements have been made simultaneously and routinely at the site by as many as three different types of instruments, including two pointing Sun radiometers. Scatterplots indicate high correlations and small biases consistent with earlier comparisons. The early part of this 16 year record had a disturbed stratosphere with residual Mt. Pinatubo aerosols, followed by the cleanest stratosphere in decades. As such, the last 13 years of the record reflect changes that have occurred predominantly in the troposphere. The field calibration technique is briefly described and compared to Langley calibrations from Mauna Loa Observatory. A modified cloud‐ screening technique is introduced that increases the number of daily averaged AODs retrieved annually to about 250 days compared with 175 days when a more conservative method was employed in earlier studies. AODs are calculated when the air mass is less than six; that is, when the Sun’s elevation is greater than 9.25°. The more inclusive cloud screen and the use of most of the daylight hours yield a data set that can be used to more faithfully represent the true aerosol climate for this site. The diurnal aerosol cycle is examined month‐by‐month to assess the effects of an aerosol climatology on the basis of infrequent sampling such as that from satellites.

https://www.bnl.gov/envsci/pubs/pdf/2010/BNL-90707-2010-JA.pdf

James Schlemmer

Papers

Validation of short and medium term operational solar radiation forecasts in the US

Parameterization of site-specific short-term irradiance variability

Multiyear measurements of aerosol optical depth in the Atmospheric Radiation Measurement and Quantitative Links programs

Climatology of aerosol optical depth in north-central Oklahoma: 1992-2008

Deploying effectively dispatchable PV on reservoirs: Comparing floating PV to other renewable technologies