scholarly journals Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1671 ◽  
Author(s):  
Tyler McCandless ◽  
Pedro Angel Jiménez
Keyword(s):  
Random Forest ◽  
Solar Irradiance ◽  
Selection Process ◽  
Weather Prediction ◽  
Clear Sky ◽  
Spatio Temporal ◽  
Using Data ◽  
Nwp Model ◽  
Cloud Mask ◽  
Power Forecasting

In order for numerical weather prediction (NWP) models to correctly predict solar irradiance reaching the earth’s surface for more accurate solar power forecasting, it is important to initialize the NWP model with accurate cloud information. Knowing where the clouds are located is the first step. Using data from geostationary satellites is an attractive possibility given the low latencies and high spatio-temporal resolution provided nowadays. Here, we explore the potential of utilizing the random forest machine learning method to generate the cloud mask from GOES-16 radiances. We first perform a predictor selection process to determine the optimal predictor set for the random forest predictions of the horizontal cloud fraction and then determine the appropriate threshold to generate the cloud mask prediction. The results show that the random forest method performs as well as the GOES-16 level 2 clear sky mask product with the ability to customize the threshold for under or over predicting cloud cover. Further developments to enhance the cloud mask estimations for improved short-term solar irradiance and power forecasting with the MAD-WRF NWP model are discussed.

scholarly journals Assessment of the GOES-16 Clear Sky Mask Product over the Contiguous USA Using CALIPSO Retrievals

2020 ◽  
Vol 12 (10) ◽  
pp. 1630
Author(s):  
Pedro A. Jiménez
Keyword(s):  
Weather Prediction ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Clear Sky ◽  
The North ◽  
Attractive Option ◽  
Spatio Temporal ◽  
Sky Conditions ◽  
Cloud Mask ◽  
Horizontal Grid

Cloud initialization is a challenge in numerical weather prediction. Probably the most relevant observations for this task come from geostationary satellites. These satellites provide the cloud mask with high spatio-temporal resolution and low latencies. The low latency is an attractive option for nowcasting systems such as the solar irradiance nowcasting model MAD-WRF. In this study we examine the potential of using the cloud mask from the GOES-16 satellite over the contiguous U.S. for this particular application. With this aim, the GOES-16 cloud mask product is compared against CALIPSO retrievals during a two year period. Both the GOES-16 data and the CALIPSO retrievals are interpolated to a grid that covers the contiguous U.S. at 9 km of horizontal grid spacing that is being used in MAD-WRF nowcasts. Results indicate a probability of detection, or accuracy, of all sky conditions of 86.0%. However, the accuracy is higher for cloud detections, 90.9% than for clear sky detections 74.8%. The lower performance of clear sky retrievals is a result of missdetections during daytime. This is especially clear for summer, and for regions to the north of parallel 36 during winter. However, regions to the south of parallel 36 show acceptable performance during both daytime and nighttime. It is over these regions wherein the cloud mask product should show its largest potential to enhance the cloud initialization in the MAD-WRF model.

scholarly journals Building the Sun4Cast System: Improvements in Solar Power Forecasting

2018 ◽  
Vol 99 (1) ◽  
pp. 121-136 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Branko Kosović ◽  
Tara Jensen ◽  
Jeffrey K. Lazo ◽  
Jared A. Lee ◽  
...  
Keyword(s):  
Decision Making ◽  
Value Chain ◽  
Solar Irradiance ◽  
Collaborative Design ◽  
Solar Power ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Solar Array ◽  
Solar Power Forecasting ◽  
Power Forecasting

Abstract As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

scholarly journals Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

2020 ◽  
Vol 32 (1) ◽  
pp. 39-53
Author(s):  
Dalia Shanshal ◽  
Ceni Babaoglu ◽  
Ayşe Başar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Injury Severity ◽  
Predictive Analytics ◽  
Machine Learning Techniques ◽  
Lasso Regression ◽  
Severe Injuries ◽  
Factors Affecting ◽  
Spatio Temporal ◽  
Using Data

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

scholarly journals IEA Wind Task 36 Forecasting

2020 ◽  
Author(s):  
Gregor Giebel ◽  
Will Shaw ◽  
Helmut Frank ◽  
Pierre Pinson ◽  
Caroline Draxl ◽  
...  
Keyword(s):  
Wind Power ◽  
Selection Process ◽  
Meteorological Data ◽  
Weather Prediction ◽  
International Energy Agency ◽  
General Situation ◽  
Energy Agency ◽  
Wind Power Forecasting ◽  
Probabilistic Forecasts ◽  
Power Forecasting

<p>Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Wind Task on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, ...), forecast vendors and forecast users.<br>Collaboration is open to IEA Wind member states, 12 countries are already therein.</p><p>The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks for NWP models. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.</p><p>The main result is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, and Designing and executing forecasting benchmarks and trials, and their Evaluation) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls which we have seen over the years, are avoided. <br><br>Other results include a paper on possible uses of uncertainty forecasts, an assessment of the uncertainty chain within the forecasts, and meteorological data on an information portal for wind power forecasting. This meteorological data is used for a benchmark exercise, to be announced at the conference. The poster will present the latest developments from the Task, and announce the next activities.</p>

Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Marco Pierro ◽  
Francesco Bucci ◽  
Matteo De Felice ◽  
Enrico Maggioni ◽  
Alessandro Perotto ◽  
...  
Keyword(s):  
Power Generation ◽  
Stochastic Models ◽  
Solar Power ◽  
Weather Prediction ◽  
Wrf Model ◽  
Machine Learning Techniques ◽  
Electricity Grid ◽  
Clear Sky ◽  
Pv Power Generation ◽  
Power Forecasting

Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of resource variability caused by high solar power penetration into the electricity grid. Two main methods are currently used for PV power generation forecast: (i) a deterministic approach that uses physics-based models requiring detailed PV plant information and (ii) a data-driven approach based on statistical or stochastic machine learning techniques needing historical power measurements. The main goal of this work is to analyze the accuracy of these different approaches. Deterministic and stochastic models for day-ahead PV generation forecast were developed, and a detailed error analysis was performed. Four years of site measurements were used to train and test the models. Numerical weather prediction (NWP) data generated by the weather research and forecasting (WRF) model were used as input. Additionally, a new parameter, the clear sky performance index, is defined. This index is equivalent to the clear sky index for PV power generation forecast, and it is here used in conjunction to the stochastic and persistence models. The stochastic model not only was able to correct NWP bias errors but it also provided a better irradiance transposition on the PV plane. The deterministic and stochastic models yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively.

scholarly journals The HIRLAM fast radiation scheme for mesoscale numerical weather prediction models

2017 ◽  
Vol 14 ◽  
pp. 195-215 ◽  
Author(s):  
Laura Rontu ◽  
Emily Gleeson ◽  
Petri Räisänen ◽  
Kristian Pagh Nielsen ◽  
Hannu Savijärvi ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Surface Radiation ◽  
Numerical Weather ◽  
Clear Sky ◽  
Radiation Output ◽  
Dry Conditions ◽  
Nwp Model

Abstract. This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the model, without compromising on computational efficiency. In mesoscale models fast interactions between clouds and radiation and the surface and radiation can be of greater importance than accounting for the spectral details of clear-sky radiation; thus calling the routines more frequently can be of greater benefit than the deterioration due to loss of spectral details. Fast but physically based radiation parametrizations are expected to be valuable for high-resolution ensemble forecasting, because as well as the speed of their execution, they may provide realistic physical perturbations. Results from single-column diagnostic experiments based on CIRC benchmark cases and an evaluation of 10 years of radiation output from the FMI operational archive of HIRLAM forecasts indicate that HLRADIA performs sufficiently well with respect to the clear-sky downwelling SW and longwave LW fluxes at the surface. In general, HLRADIA tends to overestimate surface fluxes, with the exception of LW fluxes under cold and dry conditions. The most obvious overestimation of the surface SW flux was seen in the cloudy cases in the 10-year comparison; this bias may be related to using a cloud inhomogeneity correction, which was too large. According to the CIRC comparisons, the outgoing LW and SW fluxes at the top of atmosphere are mostly overestimated by HLRADIA and the net LW flux is underestimated above clouds. The absorption of SW radiation by the atmosphere seems to be underestimated and LW absorption seems to be overestimated. Despite these issues, the overall results are satisfying and work on the improvement of HLRADIA for the use in HARMONIE-AROME NWP system is ongoing. In a HARMONIE-AROME 3-D forecast experiment we have shown that the frequency of the call for the radiation parametrization and choice of the parametrization scheme makes a difference to the surface radiation fluxes and changes the spatial distribution of the vertically integrated cloud cover and precipitation.

Spatio-Temporal Prediction Using Data Mining Tools

2011 ◽  
pp. 251-271 ◽  
Author(s):  
Margaret H. Dunham ◽  
Nathaniel Ayewah ◽  
Zhigang Li ◽  
Kathryn Bean ◽  
Jie Huang
Keyword(s):  
Data Mining ◽  
Input Data ◽  
Weather Prediction ◽  
Difficult Problem ◽  
Flood Prediction ◽  
Temporal Prediction ◽  
Spatio Temporal ◽  
Using Data ◽  
Prediction Problems ◽  
Mining Tools

The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that come to play in spatio-temporal prediction problems. We describe some recent work in the area of flood prediction to illustrate the use of sophisticated data mining techniques that have been examined as possible solutions. We argue the need for further data mining research to attack this difficult problem. This chapter is directed toward professionals and researchers who may wish to engage in spatio-temporal prediction.

scholarly journals Multifractal evaluation of simulated precipitation intensities from the COSMO NWP model

2017 ◽  
Vol 17 (23) ◽  
pp. 14253-14273 ◽  
Author(s):  
Daniel Wolfensberger ◽  
Auguste Gires ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer ◽  
Alexis Berne
Keyword(s):  
Extreme Values ◽  
Weather Prediction ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Scale Invariant ◽  
Simulated Precipitation ◽  
Wide Range ◽  
Synoptic Conditions ◽  
Spatio Temporal ◽  
Nwp Model

Abstract. The framework of universal multifractals (UM) characterizes the spatio-temporal variability in geophysical data over a wide range of scales with only a limited number of scale-invariant parameters. This work aims to clarify the link between multifractals (MFs) and more conventional weather descriptors and to show how they can be used to perform a multi-scale evaluation of model data. The first part of this work focuses on a MF analysis of the climatology of precipitation intensities simulated by the COSMO numerical weather prediction model. Analysis of the spatial structure of the MF parameters, and their correlations with external meteorological and topographical descriptors, reveals that simulated precipitation tends to be smoother at higher altitudes, and that the mean intermittency is mostly influenced by the latitude. A hierarchical clustering was performed on the external descriptors, yielding three different clusters, which correspond roughly to Alpine/continental, Mediterranean and temperate regions. Distributions of MF parameters within these three clusters are shown to be statistically significantly different, indicating that the MF signature of rain is indeed geographically dependent. The second part of this work is event-based and focuses on the smaller scales. The MF parameters of precipitation intensities at the ground are compared with those obtained from the Swiss radar composite during three events corresponding to typical synoptic conditions over Switzerland. The results of this analysis show that the COSMO simulations exhibit spatial scaling breaks that are not present in the radar data, indicating that the model is not able to simulate the observed variability at all scales. A comparison of the operational one-moment microphysical parameterization scheme of COSMO with a more advanced two-moment scheme reveals that, while no scheme systematically outperforms the other, the two-moment scheme tends to produce larger extreme values and more discontinuous precipitation fields, which agree better with the radar composite.

scholarly journals 3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

2020 ◽  
Vol 12 (6) ◽  
pp. 920 ◽  
Author(s):  
Sabrina Gentile ◽  
Francesco Di Paola ◽  
Domenico Cimini ◽  
Donatello Gallucci ◽  
Edoardo Geraldi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Weather Prediction ◽  
National Research Council ◽  
Mean Bias Error ◽  
Bias Error ◽  
Clear Sky ◽  
Sky Conditions ◽  
The Impact

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

Spatio-Temporal Prediction Using Data Mining Tools

2008 ◽  
pp. 1400-1415
Author(s):  
Margaret H. Dunham ◽  
Nathaniel Ayewah ◽  
Zhigang Li ◽  
Kathryn Bean ◽  
Jie Huang
Keyword(s):  
Data Mining ◽  
Input Data ◽  
Weather Prediction ◽  
Difficult Problem ◽  
Flood Prediction ◽  
Temporal Prediction ◽  
Spatio Temporal ◽  
Using Data ◽  
Prediction Problems ◽  
Mining Tools

The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that come to play in spatio-temporal prediction problems. We describe some recent work in the area of flood prediction to illustrate the use of sophisticated data mining techniques that have been examined as possible solutions. We argue the need for further data mining research to attack this difficult problem. This chapter is directed toward professionals and researchers who may wish to engage in spatio-temporal prediction.

Sign in / Sign up

Export Citation Format

Share Document