Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “solar forecasting”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Forecasts of solar and geomagnetic activity

Forecasts of solar and geomagnetic activity are critical since these quantities are such important inputs to the thermospheric density models. At this time in the history of solar science there is no way to make such a forecast from first principles. Physical theory applied to the Sun is developing rapidly, but is still primitive. Techniques used for forecasting depend upon the observations over about 130 years, which is only twelve solar cycles. It has been noted that even-numbered cycles systematically tend to be smaller than the odd-numbered ones by about 20 percent. Another observation is that for the last 12 cycle pairs, an even-numbered sunspot cycle looks rather like the next odd-numbered cycle, but with the top cut off. These observations are examples of approximate periodicities that forecasters try to use to achieve some insight into the nature of an upcoming cycle. Another new and useful forecasting aid is a correlation that has been noted between geomagnetic indices and the size of the next solar cycle. Some best estimates are given concerning both activities.

Joselyn, Joann↗
NASA NTRS2011

Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

MacNeice, Peter↗
NASA NTRS2019

Characterization of Subsurface Flow Dynamics for Forecasting of Solar Activity

Evolution of large-scale magnetic field structures in the solar photosphere and corona is controlled by motions beneath the visible surface of the Sun. Subsurface plasma flows play a critical role in formation and evolution of active regions and their activity. We analyze subsurface flow maps provided by the local helioseismology pipeline from the Helioseismic and Magnetic Imager (HMI) data on board the Solar Dynamics Observatory, and investigate links between flow characteristics and magnetic activity. The primary goal is to determine flow descriptors, which can improve solar activity forecasts. In particular, by employing machine learning classifiers, we test how the flow helicity and velocity shear descriptors can improve the prediction of initiation of flares and CME eruptions.

Kosovichev, Alexander↗
DOE OSTI2022

Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $\$100$ million per year for a CAISO-size system).

14 SOLAR ENERGY↗
NASA NTRS1972

Solar flare forecasts based on mm-wavelength measurements

Solar radio temperature contour maps at a wavelength of 3.3 mm are analyzed to develop a method of forecasting solar flares of classes 1N and 1B. Fundamental to the analysis is the demonstration that most of the radio sources associated with the sunspot-plage systems are not spatially resolved by the radio telescope and, hence, only the peak temperature enhancements due to these local radio sources are employed. Evidence is presented indicating that flare-prone plage regions, which belong to recurrent families of flare-productive regions, exhibit a relationship between flare production and radio temperature characteristics considerably unlike that exhibited by virgin plage regions. The results are interpreted by means of graphs which permit the inference of class 1B or 1N flare probabilities based on the peak temperature enhancements measured at 3.3 mm. In particular, the forecast obtained is the probability that a given flare-prone plage region will produce a class 1B or 1N flare (two quoted probabilities) during the period of its disk transit.

White, K. P., III↗
DOE OSTI2020

Predictive and Cooperative Voltage Control with Probabilistic Load and Solar Generation Forecasting

This paper proposes predictive cooperative voltage control method in a power system with high penetration of photovoltaic (PV) units. Cooperative distributed control of the reactive power output of PV inverters is coordinated with operation of voltage regulators (VRs) to maintain system voltages within an appropriate bandwidth. Probabilistic forecasting of the solar power generation and the loads is applied to estimate voltage changes which, in turn, are used to set the VR tap positions for preventing large voltage fluctuations with the lowest risk considering the voltage distribution estimation. The fine tuning of voltage adjustment is achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on the modified IEEE 123-node test feeder with high PV penetration using real insolation data and with constant loads replaced by several different load profiles. Simulation results demonstrate the effectiveness of the coordinated approach for voltage control with cooperative PV and predictive VR controls taking into account probabilistic load and solar power forecasts.

Cooperative Control↗
DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗
DOE OSTI2022

Evaluation of WRF-Solar Cloud Forecast Using the NSRDB: Preprint

Cloud forecast is a crucial component in predicting solar irradiance from numerical weather prediction (NWP) models. Assessing cloud properties from the NWP models requires significant work due to the need for high-quality data, spatial analysis covering model extent, and detailed analysis of model performance for different types of clouds. This study presents an evaluation of the WRF-Solar cloud forecast using the National Solar Radiation Database (NSRDB). We propose an evaluation framework applied to a single model prediction as well as ensemble-based forecasts. Various cloud detection metrics are calculated when comparing with the satellite-derived dataset. The mismatched clouds from the WRF-Solar model are quantified using nine cloud types classified by cloud top height and cloud optical depth. The results based on the WRF-Solar forecasts covering the entire U.S. for the full year of 2018 shows mismatched cloud frequency in the range of 8% - 46% for thick and high-level (deep convective) to thin and low-level (cumulus) clouds.

cloud mask forecast↗
DOE OSTI2022

Short-term solar radiation forecast using total sky imager via transfer learning

Ground-based sky cameras, which capture hemispherical images, have been extensively used for localized monitoring of clouds. This paper proposes a short-term forecasting approach based on transfer learning using Total Sky-Imager (TSI) images of the Southern Great Plains (SGP) site obtained from the Atmospheric Radiation Measurement (ARM) dataset. An accurate estimation of solar irradiance using TSI is key for short-term solar energy generation forecasting and optimal energy consumption planning. We make use of deep neural network architectures such as AlexNet and ResNet-101 to extract the underlying deep convolution features from TSI images and then train using an ensemble learning approach to model and forecast solar radiation. We demonstrate the performance of the proposed approach by showcasing the best and worst cases. Thus, the transfer learning approach significantly reduces the time and resources required for modeling solar radiation. We outperform with reference to another state-of-art technique for solar modeling using TSI images at different forecast lead times.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Problems in the Forecasting of Solar Particle Events for Manned Missions

Manned spaecraft will require a much improved ability to forecast solar particle events. The lead time required will depend on the use to which the forecast is put. Here we discuss problems of forecasting with the lead times of hours to weeks.

forcast problem coronal mass ejections (CMEs) low ↗
DOE OSTI2021

Pro2R: Procurement of Ramping Product and Regulation in CAISO Using Probabilistic Solar Power Forecasts

This presentation summarizes the objectives and findings from the project titled, "Pro2R: Coordinated Procurement of Ramping Product & Regulation in CAISO Using Probabilistic Solar Power Forecasts," funded by DOE SETO. The goal is to develop cutting edge probabilistic solar power forecasting methods, and integrate them into ISO market operations. The team has integrated in two ways: 1) to procure ramping product and regulation requirements (ancillary services) in the California ISO market, and 2) to develop an open source visitation and ramp alert related situational awareness using probabilistic forecasts. This deck also summarizes the results from the use of machine learning methods to relate probabilistic solar forecasts to the the needs for flexible ramp product in the California ISO system.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

HAIMOS Ensemble Forecasts for Intra-day and Day- Ahead GHI, DNI and Ramps

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

14 SOLAR ENERGY↗
NASA NTRS1979

Long-term forecasting of solar activity

Corrections to the forecast of the basic parameters of the 21st cycle are described. Estimates about the level of solar activity in the 22nd cycle are presented.

Zhen-Tao, X.↗
DOE OSTI2020

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗
DOE OSTI2024

Enhancing Solar Power Forecasting with Regularized Constrained Quantile Regression Averaging and Bootstrapping Techniques

Probabilistic solar power forecasting (SPF) plays an essential role in optimizing power-grid operations by quantifying the forecast uncertainty. To improve the accuracy and robustness of probabilistic SPF, this paper introduces the regularized constrained quantile regression averaging (rCQRA) method to combine outputs from multiple PSPF models. In addition, a bootstrapping method was used to quantify model uncertainty, providing insights into the reliability and significance of each ensemble component. To evaluate its efficacy, the proposed rCQRA method is used to integrate four PSPF methods. The resulting SPF models are trained and validated using a real-world six-year dataset from a rooftop solar plant in the USA. The performance of the proposed rCQRA method is evaluated and compared with two benchmark methods under three categories of weather conditions. It is shown that the rCQRA method has superior performance in its forecast reliability, sharpness, and accuracy.

Ensemble learning, probabilistic solar power forec↗
NASA NTRS1979

Analysis of regression methods for solar activity forecasting

The paper deals with the potential use of the most recent solar data to project trends in the next few years. Assuming that a mode of solar influence on weather can be identified, advantageous use of that knowledge presumably depends on estimating future solar activity. A frequently used technique for solar cycle predictions is a linear regression procedure along the lines formulated by McNish and Lincoln (1949). The paper presents a sensitivity analysis of the behavior of such regression methods relative to the following aspects: cycle minimum, time into cycle, composition of historical data base, and unnormalized vs. normalized solar cycle data. Comparative solar cycle forecasts for several past cycles are presented as to these aspects of the input data. Implications for the current cycle, No. 21, are also given.

Lundquist, C. A.↗