A5051722266- Energy Load and Power Forecasting
- Solar Radiation and Photovoltaics
- Meteorological Phenomena and Simulations
- Photovoltaic System Optimization Techniques
- Wind Energy Research and Development
- Wind and Air Flow Studies
- Atmospheric aerosols and clouds
- Climate variability and models
- Radiative Heat Transfer Studies
- Solar and Space Plasma Dynamics
- Fire effects on ecosystems
- Ionosphere and magnetosphere dynamics
- Remote Sensing in Agriculture
- Landslides and related hazards
- Precipitation Measurement and Analysis
- Plant Water Relations and Carbon Dynamics
- Hydrological Forecasting Using AI
- Atmospheric chemistry and aerosols
- Power Systems and Renewable Energy
- Surface Roughness and Optical Measurements
- Engineering Applied Research
- Advanced Measurement and Detection Methods
- Oil Spill Detection and Mitigation
- Atmospheric and Environmental Gas Dynamics
- Impact of Light on Environment and Health
Tomorrows Children’s Fund
2023
The Tomorrow Companies Inc. (United States)
2023
Primary Source
2020-2022
Pennsylvania State University
2011-2020
NSF National Center for Atmospheric Research
2015-2020
Research Applications (United States)
2016-2020
Research Applications Laboratory
2017
Walker (United States)
2015
University Corporation for Atmospheric Research
2011
Abstract As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting this highly variable renewable resource. Thus, a team researchers from public, private, and academic sectors partnered develop assess new system, Sun4Cast. The partnership focused on improving decision-making for utilities independent system operators, ultimately resulting in improved stability cost savings consumers. project followed value chain approach...
Abstract Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, existence of roughness sublayer and absence well-defined viscous layer. Exchanges with surface therefore dominated turbulent fluxes. In numerical models for flows, fluxes must be specified at surface; however, not known priori parametrized. Atmospheric flow models, including global circulation, limited area large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to...
The National Center for Atmospheric Research (NCAR) has a long history of applying machine learning to weather forecasting challenges. Dynamic Integrated foreCasting (DICast®) System was one the first automated engines. It is now in use quite few companies with many applications. Some applications being accomplished at NCAR that include DICast and other artificial intelligence technologies renewable energy, surface transportation, wildland fire forecasting.
A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in operation and grid integration. This paper describes such a being developed for the Shagaya Renewable Energy Park, which is by State of Kuwait. The park contains wind turbines, photovoltaic panels, concentrated solar technologies storage capabilities. fully operational Kuwait Prediction System (KREPS) employs (AI) multiple portions structure processes, both short-range (i.e., next six...
Abstract The Sun4Cast solar power forecasting system, designed to predict irradiance and generation at farms, is composed of several component models operating on both the nowcasting (0–6 h) day-ahead forecast horizons. different include a statistical model (StatCast), two satellite-based [the Cooperative Institute for Research in Atmosphere Nowcast (CIRACast) Multisensor Advection-Diffusion (MADCast)], numerical weather prediction (WRF-Solar). It important better understand assess strengths...
Abstract Ensembles of numerical weather prediction (NWP) model predictions are used for a variety forecasting applications. Such ensembles quantify the uncertainty because spread in ensemble is correlated to forecast uncertainty. For atmospheric transport and dispersion wind energy applications particular, NWP should accurately represent low-level mean wind. To adequately sample probability density function (PDF) state, it necessary account several sources Limited computational resources...
Abstract This paper describes the development and testing of a cloud-regime-dependent short-range solar irradiance forecasting system for predictions 15-min-average clearness index (global horizontal irradiance). regime-dependent artificial neural network (RD-ANN) classifies cloud regimes with k -means algorithm on basis combination surface weather observations, GOES-East satellite data. The ANNs are then trained each regime to predict index. RD-ANN improves over mean absolute error baseline...
In order for numerical weather prediction (NWP) models to correctly predict solar irradiance reaching the earth’s surface more accurate power forecasting, it is important initialize NWP model with cloud information. Knowing where clouds are located first step. Using data from geostationary satellites an attractive possibility given low latencies and high spatio-temporal resolution provided nowadays. Here, we explore potential of utilizing random forest machine learning method generate mask...
Abstract Wildland fire decision support systems require accurate predictions of wildland spread. Fuel moisture content (FMC) is one the important parameters controlling rate spread fire. However, dead FMC measurements are provided by a relatively sparse network remote automatic weather stations (RAWS), while live infrequently measured manually. We developed high resolution, gridded, real-time data sets that did not previously exist for assimilation into operational prediction based on ML....
This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a approach in climate with diverse cloud conditions. study approaches for prediction at Shagaya Renewable Energy Park Kuwait, which is arid desert characterized by abundant sunshine. The regime-dependent artificial...
One way to mitigate the variability of wind and solar power generation is install corresponding plants in nearby locations. For example, Kuwait, facility at Shagaya Renewable Energy Park located a desert area with both photovoltaic panels turbines that allow continuous renewable energy throughout day. The National Center for Atmospheric Research (NCAR) has developed system generate probabilistic predictions facility. These are based on analog ensemble technique post-processes speed...
Electrical system operators utilizing wind energy production need accurate power forecasts to prepare for changes in production. To understand the forecast problem and sources of uncertainty, a climatology region interest is needed. For Shagaya Renewable Energy Park Kuwait, seasonal diurnal patterns atmospheric phenomena that cause them are identified using observations from meteorological towers, surface weather stations, turbines. A setup conducive shamals increases hub-height speed by up...
A major issue for developing post-processing methods NWP forecasting systems is the need to obtain complete training datasets. Without a dataset, it can become difficult, if not impossible, train and verify statistical techniques, including ensemble consensus schemes. In addition, when forecast data are missing, real-time use of weighting scheme becomes difficult quality uncertainty information derived from reduced. To ameliorate these problems, an analysis treatment missing in model...
Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, wind speed forecast for plant forecasted converted provide an estimate of expected generating capacity plant. The average function underlying meteorological phenomena being predicted; however, each turbine at farm also local terrain array orientation. Conversion algorithms that assume plant, i.e., super-turbine conversion, effects...
Modern renewable energy forecasting systems blend physical models with artificial intelligence (AI). This paper describes such a system being developed for the Shagaya Renewable Energy Park in Kuwait. Kuwait Prediction System uses both modeling and machine learning to produce forecasts short-range (i.e. next six hours) as well several days out. These work together implement best of our knowledge atmospheric flow actual observations at site interest. The include numerical weather prediction,...
Although cloud base height is a relevant variable for many applications, including aviation, it not routinely monitored by current geostationary satellites. This probably consequence of the difficulty providing reliable estimations from visible and infrared radiances imagers. We hypothesize that existing algorithms suffer accumulation errors upstream retrievals necessary to estimate height, this hampers higher predictability in be achieved. To test hypothesis, we trained statistical model...