User Perspectives on Project Feasibility Data

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "User Perspectives on Project Feasibility Data"

Transcription

1 User Perspectives on Project Feasibility Data Marcel Šúri Tomáš Cebecauer GeoModel Solar s.r.o., Bratislava, Slovakia Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [1]

2 Solar resource information - user requirements Global coverage, harmonized, validated Data available at any location Long-climate record (up to years) harmonized and without gaps High accuracy, low uncertainty High level of detail (temporal, spatial) Modern data products (long-term averages, RMY, time series) Real-time data supply: historical, monitoring, nowcasting, forecasting + Meteo and other geodata for energy modeling All this is possible with satellite-based data, supported by high-quality ground measurements! Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [2]

3 Project stages 1. Prospecting, prefeasibility and site assessment 2. Feasibility, design optimization, financing and due diligence 3. Performance assessment 4. Management of solar power and energy markets (not considered in this presentation) Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [3]

4 Project stages 1. Prospecting, prefeasibility and site assessment 2. Feasibility, design optimization, financing and due diligence 3. Performance assessment 4. Management of solar power and energy markets (not considered in this presentation) Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [4]

5 Prospecting, prefeasibility and site assessment Data needed: Representative and homogeneous annual long-term averages and aggregated monthly statistics of satellite-based data at high spatial resolution At least 10 years of data are needed to represent climate reliably or uncertainty has to be increased Terrain and air temperature are needed for energy modeling Other GIS data (infrastructure, population, etc.) for country analysis Uncertainty (bias) for long-term annual values to be typically expected: DNI ±4 to 15% GHI ±2 to 7% Uncertainty is higher in the complex land cover (land/sea/desert/islands), in mountains (snow/ice) and high latitudes, and in regions with extreme aerosols/humidity Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [5]

6 Prospecting, prefeasibility and site assessment Services needed: Fast access: on-the-click information Interactive online tools for energy simulation Automated computer access Preliminary assessment site reports Country reports - resource potential analysis Maps, GIS data Uncertainty in energy modeling contributes by few more percent; however some simple algorithms produce systematic errors Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [6]

7 Project stages 1. Prospecting, prefeasibility and site assessment 2. Feasibility, design optimization, financing and due diligence 3. Performance assessment 4. Management of solar power and energy markets (not considered in this presentation) Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [7]

8 Feasibility, design optimization, financing and due diligence Data needed: Site-specific solar and meteo time series with long-term data record Representative Meteorological Year (RMY) Ancillary meteo data (air temperature, relative humidity, wind speed and direction) High-resolution terrain data Services needed: Site adaptation of satellite-based data by correlating them to local solar measurements Generation of Representative Meteorological Year Bankable reports: Site analysis of solar resource, Energy yield study Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [8]

9 Feasibility, design optimization, financing and due diligence Time series This is the only bankable data, which can provide full climate statistics average, median percentiles, interannual variability, P(50), P(90) expectances, probability distributions, etc. 12 to 20+ years of high quality data are available worldwide at primary resolution of approx. 3 to 5 km Quality parameters: Minimum bias, low RMSD (if possible adapted for the site) Representative distribution statistics skewed distribution of parameters (GHI and DNI) results in errors when simulating tilted solar radiation or energy yield For due diligence and financing, the satellite-based time series and derived data products have to be validated using independent sites from the similar climate zone, or site adapted (correlated) with local measurements Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [9]

10 Site adaptation of satellite-based time series Ground measurements available for a short time period (few months, 1-2 years) They are correlated with time series of satellite-derived irradiance to: Correct systematic errors (reduce bias) Match data frequency distribution Conditions to be fulfilled for successful adaptation: Systematic deviation in satellite data should exist Magnitude of deviation is invariant in time High quality ground measurements are available Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [10]

11 Site adaptation of satellite-based time series Example: Tamanrasset (Algeria) Simple bias correction Original DNI ground satellite data scatterplot: Bias: -4.2% Correction of frequency distribution Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [11]

12 Site adaptation of satellite-based time series Example: Tamanrasset (Algeria) Ground measurements must be of high quality and must be properly quality-checked. Result of the procedure: Eliminated (reduced) bias - when compared to local measurements reduced RMSD improved statistical distribution of values Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [12]

13 Assessment of Solar Resource. Upington Solar Park, South Africa. Reference No /2011 Representative Meteorological Year (RMY) Two data sets are derived from site-adapted time series one for P(50) and one for P(90). In assembling RMY, the values of DNI, GHI and Air Temperature are only considered, where the weights are set as follows: 0.6 is given to DNI, 0.4 to GHI, and 0.1 to Air Temperature (divided by the total of 1.1). Includes The P(50) hourly RMY data data set represents, of one for each representative/typical month, the average climate conditions year and the most derived representative from the time cumulative distribution function, therefore extreme situations (e.g. extremely cloudy weather) are not represented this dataset. Therefore, to capture all possible weather situations it is recommended in power series production representing simulations to use long full (17 period years) time series of the data. The P(90) RMY data set represents for each month the climate conditions which after summarization of DNI for the whole year result in the value close to P(90) derived by statistical analysis of uncertainties and interannual variability (the conservative DNI value 2729 kwh/m It is constructed on a monthly basis, comparing months of individual years with 2, see Section 10, Tab. 16). Thus RMY for P(90) represents the year with the lowest annual value of DNI over the period of 17 years. Both RMY data sets include the following parameters: two long-term monthly characteristics: cumulative distribution function and the Direct Normal Irradiance, DNI [W/m 2 ] Global Horizontal Irradiance, GHI [W/m mean. The representative months 2 ] are concatenated into RMY Diffuse Horizontal Irradiance, Diff [W/m 2 ] Azimuth and solar angle [ ] RMY is comparable Air temperature at 2 to metres, the Temper TMY [ C] file, only the weighting is tuned to meet the Wind speed at 10 metres, Wspeed [m/s] modeling needs of either PV (focus on GHI) or CSP/CPV (focus on DNI). Wind direction, Wdir [ ] Relative air humidity, Rh [%] Fig. 11: Snapshot of the P(50) Representative Meteorological Year, RMY Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [13]

14 Representative Meteorological Year (RMY) Representative Meteorological Year: P(50) RMY data set represents, for each month, the average climate conditions and the most representative cumulative distribution function, therefore extreme situations (e.g. extremely cloudy weather) are not represented in this dataset. To characterize year with very conservative values of solar resource P(90) RMY data set can be derived. It represents for each month the climate conditions which after summarization of DNI (GHI) for the whole year result in the value close to P(90). The P(90) annual value is derived from the uncertainty and interannual variability, thus RMY for P(90) represents closely a year with the lowest annual value of DNI (GHI) over the longer period. There may be some other types of RMY constructed, depending on the criteria Solar resource data in RMY can be supplemented by air temperature, relative humidity, wind speed, and wind direction Meteo data can be supplied from meteorological models Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [14]

15 Calculation of tilted irradiance and energy simulations These type of calculations have non-linear nature and therefore the results are affected if proper distribution of values (direct and diffuse irradiance and temperature) is not maintained This is a case of average daily profiles and also synthetic time series. Therefore, it is not advised to apply these older data products, if RMY or full time series can be used. Satellite-derived time series and RMY are available today for almost any location worldwide, except polar regions Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [15]

16 Project stages 1. Prospecting, prefeasibility and site assessment 2. Feasibility, design optimization, financing and due diligence 3. Performance assessment 4. Management of solar power and energy markets (not considered in this presentation) Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [16]

17 Performance assessment Satellite-derived time series have numerous advantages (compared to ground sensors): Good quality, stable radiometry Available for any location Time availability 99.5%, just few gaps have to be filled by intelligent algorithms Known quality and uncertainty over large areas No problems with pollution, misalignment, data cleaning, calculation of timeintegrated statistics Therefore they can be used for: Performance assessment of power plants Validation of on-site measured irradiance Easy and cheap service for production evaluation of PV systems Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [17]

18 Conclusions Traditional approaches based on interpolation of point measurements and application of synthetic generators are substituted by satellite-derived time series which have a number of advantages: They are available for (almost) any site globally Have often better overall quality and reliability High-quality data products can be derived: Representative Meteorological Year for planning and design Aggregated statistics for reporting Customized time series for monitoring and system performance assessment Complementary data to ground measurements In the absence of high quality ground measurements satellite-based data offer the only alternative for system monitoring and performance assessment. Solar Resources and Forecasting Workshop, NREL, Boulder CO, June 2011 [18]

Solar Input Data for PV Energy Modeling

Solar Input Data for PV Energy Modeling June 2012 Solar Input Data for PV Energy Modeling Marie Schnitzer, Christopher Thuman, Peter Johnson Albany New York, USA Barcelona Spain Bangalore India Company Snapshot Established in 1983; nearly 30

More information

Site Assessment of Solar Resource

Site Assessment of Solar Resource Site Assessment of Solar Resource Upington Solar Park Province Northern Cape, South Africa rev. 2 Date: 14 June 2011 Customer: Supplier: Stellenbosch University Contact: Mr. Riaan Meyer Centre for Renewable

More information

REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES

REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES Mitigating Energy Risk through On-Site Monitoring Marie Schnitzer, Vice President of Consulting Services Christopher Thuman, Senior Meteorologist Peter Johnson,

More information

CLOUD COVER IMPACT ON PHOTOVOLTAIC POWER PRODUCTION IN SOUTH AFRICA

CLOUD COVER IMPACT ON PHOTOVOLTAIC POWER PRODUCTION IN SOUTH AFRICA CLOUD COVER IMPACT ON PHOTOVOLTAIC POWER PRODUCTION IN SOUTH AFRICA Marcel Suri 1, Tomas Cebecauer 1, Artur Skoczek 1, Ronald Marais 2, Crescent Mushwana 2, Josh Reinecke 3 and Riaan Meyer 4 1 GeoModel

More information

EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS

EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS Author Marie Schnitzer Director of Solar Services Published for AWS Truewind October 2009 Republished for AWS Truepower: AWS Truepower, LLC

More information

Vaisala 3TIER Services Global Solar Dataset / Methodology and Validation

Vaisala 3TIER Services Global Solar Dataset / Methodology and Validation ENERGY Vaisala 3TIER Services Global Solar Dataset / Methodology and Validation Global Horizontal Irradiance 70 Introduction Solar energy production is directly correlated to the amount of radiation received

More information

Expert System for Solar Thermal Power Stations. Deutsches Zentrum für Luft- und Raumfahrt e.v. Institute of Technical Thermodynamics

Expert System for Solar Thermal Power Stations. Deutsches Zentrum für Luft- und Raumfahrt e.v. Institute of Technical Thermodynamics Expert System for Solar Thermal Power Stations Institute of Technical Thermodynamics Stuttgart, July 2001 - Expert System for Solar Thermal Power Stations 2 Solar radiation and land resources for solar

More information

Meteorological Forecasting of DNI, clouds and aerosols

Meteorological Forecasting of DNI, clouds and aerosols Meteorological Forecasting of DNI, clouds and aerosols DNICast 1st End-User Workshop, Madrid, 2014-05-07 Heiner Körnich (SMHI), Jan Remund (Meteotest), Marion Schroedter-Homscheidt (DLR) Overview What

More information

Solar Resource & Radiometry Tasks in Antofagasta

Solar Resource & Radiometry Tasks in Antofagasta Solar Resource & Radiometry Tasks in Antofagasta, Ph.D. aitor.marzo@uantof.cl Mauricio Trigo, Mg. Tania Varas, Mg. Antofagasta, January 14nd, 2015 Index Introduction to Solar Radiation Measurements Climates

More information

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning.

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. 31st Annual International Symposium on Forecasting Lourdes Ramírez Santigosa Martín

More information

Innovations in Understanding Wind and Solar Resource

Innovations in Understanding Wind and Solar Resource INVESTOR CONFERENCE 2010 BUILDING THE NEXT ERA OF CLEAN ENERGY Innovations in Understanding Wind and Solar Resource Mark Ahlstrom CEO, WindLogics May 4, 2010 Cautionary Statements And Risk Factors That

More information

Forecasting Solar Power with Adaptive Models A Pilot Study

Forecasting Solar Power with Adaptive Models A Pilot Study Forecasting Solar Power with Adaptive Models A Pilot Study Dr. James W. Hall 1. Introduction Expanding the use of renewable energy sources, primarily wind and solar, has become a US national priority.

More information

Solar Performance Mapping and Operational Yield Forecasting

Solar Performance Mapping and Operational Yield Forecasting Solar Performance Mapping and Operational Yield Forecasting Supported by Innovate UK Crown copyright Met Office The thing is that Project Overview A collaborative project led by the BRE National Solar

More information

Overview of BNL s Solar Energy Research Plans. March 2011

Overview of BNL s Solar Energy Research Plans. March 2011 Overview of BNL s Solar Energy Research Plans March 2011 Why Solar Energy Research at BNL? BNL s capabilities can advance solar energy In the Northeast World class facilities History of successful research

More information

PREDICTION OF PHOTOVOLTAIC SYSTEMS PRODUCTION USING WEATHER FORECASTS

PREDICTION OF PHOTOVOLTAIC SYSTEMS PRODUCTION USING WEATHER FORECASTS PREDICTION OF PHOTOVOLTAIC SYSTEMS PRODUCTION USING WEATHER FORECASTS Jure Vetršek* 1 and prof. Sašo Medved 1 1University of Ljubljana, Faculty of Mechanical Engineering, Laboratory for Sustainable Technologies

More information

Development of a. Solar Generation Forecast System

Development of a. Solar Generation Forecast System ALBANY BARCELONA BANGALORE 16 December 2011 Development of a Multiple Look ahead Time Scale Solar Generation Forecast System John Zack Glenn Van Knowe Marie Schnitzer Jeff Freedman AWS Truepower, LLC Albany,

More information

Solar Atlas for the Southern and Eastern Mediterranean

Solar Atlas for the Southern and Eastern Mediterranean Solar Atlas for the Southern and Eastern Mediterranean Carsten Hoyer-Klick 1, Lucien Wald 2, Lionel Menard 2, Philippe Blanc 2, Etienne Wey 3, Marcel Suri 4, Tomas Cebecauer 4, Thomas Huld 5, Houda Allal

More information

Is Overproduction Costing You?

Is Overproduction Costing You? Is Overproduction Costing You? A review of the impacts of solar resource data on financing and revenues for PV plants Presented by: Marie Schnitzer Vice President of Consulting Services Paul Thienpont

More information

The APOLLO cloud product statistics Web service

The APOLLO cloud product statistics Web service The APOLLO cloud product statistics Web service Introduction DLR and Transvalor are preparing a new Web service to disseminate the statistics of the APOLLO cloud physical parameters as a further help in

More information

Solar and PV forecasting in Canada

Solar and PV forecasting in Canada Solar and PV forecasting in Canada Sophie Pelland, CanmetENERGY IESO Wind Power Standing Committee meeting Toronto, September 23, 2010 Presentation Plan Introduction How are PV forecasts generated? Solar

More information

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction Jin Xu, Shinjae Yoo, Dantong Yu, Dong Huang, John Heiser, Paul Kalb Solar Energy Abundant, clean, and secure

More information

Influence of Solar Radiation Models in the Calibration of Building Simulation Models

Influence of Solar Radiation Models in the Calibration of Building Simulation Models Influence of Solar Radiation Models in the Calibration of Building Simulation Models J.K. Copper, A.B. Sproul 1 1 School of Photovoltaics and Renewable Energy Engineering, University of New South Wales,

More information

The APOLLO cloud product statistics Web service The APOLLO cloud product statistics Web service

The APOLLO cloud product statistics Web service The APOLLO cloud product statistics Web service The APOLLO cloud product statistics Web service Introduction DLR and Transvalor are preparing a new Web service to disseminate the statistics of the APOLLO cloud physical parameters as a further help in

More information

Understanding Tracker Accuracy and its Effects on CPV

Understanding Tracker Accuracy and its Effects on CPV Understanding Tracker Accuracy and its Effects on CPV M. Davis, T. Williams (GreenMountain Engineering) M. Martínez, D. Sanchéz (ISFOC) Presented at the 5 th International Conference on Solar Concentrators

More information

Use of numerical weather forecast predictions in soil moisture modelling

Use of numerical weather forecast predictions in soil moisture modelling Use of numerical weather forecast predictions in soil moisture modelling Ari Venäläinen Finnish Meteorological Institute Meteorological research ari.venalainen@fmi.fi OBJECTIVE The weather forecast models

More information

Solarstromprognosen für Übertragungsnetzbetreiber

Solarstromprognosen für Übertragungsnetzbetreiber Solarstromprognosen für Übertragungsnetzbetreiber Elke Lorenz, Jan Kühnert, Annette Hammer, Detlev Heienmann Universität Oldenburg 1 Outline grid integration of photovoltaic power (PV) in Germany overview

More information

Validation n 2 of the Wind Data Generator (WDG) software performance. Comparison with measured mast data - Flat site in Northern France

Validation n 2 of the Wind Data Generator (WDG) software performance. Comparison with measured mast data - Flat site in Northern France Validation n 2 of the Wind Data Generator (WDG) software performance Comparison with measured mast data - Flat site in Northern France Mr. Tristan Fabre* La Compagnie du Vent, GDF-SUEZ, Montpellier, 34967,

More information

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis Authors Name/s per 1st Affiliation (Author) Authors Name/s per 2nd Affiliation (Author) line 1 (of Affiliation): dept. name

More information

Satellite-Based Software Tools for Optimizing Utility Planning, Simulation and Forecasting

Satellite-Based Software Tools for Optimizing Utility Planning, Simulation and Forecasting Satellite-Based Software Tools for Optimizing Utility Planning, Simulation and Forecasting Tom Hoff, President, Research & Consulting ISES Webinar February 23, 2015 Copyright 2015 Clean Power Research,

More information

A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources

A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources Aris-Athanasios Panagopoulos1 Joint work with Georgios Chalkiadakis2 and Eftichios Koutroulis2 ( Predicting the

More information

meteonorm Global Meteorological Database

meteonorm Global Meteorological Database meteonorm Global Meteorological Database Version 7 Software and Data for Engineers, Planners and Education The Meteorological Reference for Solar Energy Applications, Building Design, Heating & Cooling

More information

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D Munn V. Shukla and P. K. Thapliyal Atmospheric Sciences Division Atmospheric and Oceanic Sciences Group Space Applications

More information

Application of global 1-degree data sets to simulate runoff from MOPEX experimental river basins

Application of global 1-degree data sets to simulate runoff from MOPEX experimental river basins 18 Large Sample Basin Experiments for Hydrological Model Parameterization: Results of the Model Parameter Experiment. IAHS Publ. 37, 26. Application of global 1-degree data sets to simulate from experimental

More information

MODELING DISTRIBUTION SYSTEM IMPACTS OF SOLAR VARIABILIY AND INTERCONNECTION LOCATION

MODELING DISTRIBUTION SYSTEM IMPACTS OF SOLAR VARIABILIY AND INTERCONNECTION LOCATION MODELING DISTRIBUTION SYSTEM IMPACTS OF SOLAR VARIABILIY AND INTERCONNECTION LOCATION Matthew J. Reno Sandia National Laboratories Georgia Institute of Technology P.O. Box 5800 MS 1033 Albuquerque, NM

More information

Solar Energy Forecasting Using Numerical Weather Prediction (NWP) Models. Patrick Mathiesen, Sanyo Fellow, UCSD Jan Kleissl, UCSD

Solar Energy Forecasting Using Numerical Weather Prediction (NWP) Models. Patrick Mathiesen, Sanyo Fellow, UCSD Jan Kleissl, UCSD Solar Energy Forecasting Using Numerical Weather Prediction (NWP) Models Patrick Mathiesen, Sanyo Fellow, UCSD Jan Kleissl, UCSD Solar Radiation Reaching the Surface Incoming solar radiation can be reflected,

More information

Solar Variability and Forecasting

Solar Variability and Forecasting Solar Variability and Forecasting Jan Kleissl, Chi Chow, Matt Lave, Patrick Mathiesen, Anders Nottrott, Bryan Urquhart Mechanical & Environmental Engineering, UC San Diego http://solar.ucsd.edu Variability

More information

Forecaster comments to the ORTECH Report

Forecaster comments to the ORTECH Report Forecaster comments to the ORTECH Report The Alberta Forecasting Pilot Project was truly a pioneering and landmark effort in the assessment of wind power production forecast performance in North America.

More information

Simulated PV Power Plant Variability: Impact of Utility-imposed Ramp Limitations in Puerto Rico

Simulated PV Power Plant Variability: Impact of Utility-imposed Ramp Limitations in Puerto Rico Simulated PV Power Plant Variability: Impact of Utility-imposed Ramp Limitations in Puerto Rico Matthew Lave 1, Jan Kleissl 2, Abraham Ellis 3, Felipe Mejia 2 1 Sandia National Laboratories, Livermore,

More information

Solar radiation data validation

Solar radiation data validation Loughborough University Institutional Repository Solar radiation data validation This item was submitted to Loughborough University's Institutional Repository by the/an author. Citation: MCKENNA, E., 2009.

More information

USING SIMULATED WIND DATA FROM A MESOSCALE MODEL IN MCP. M. Taylor J. Freedman K. Waight M. Brower

USING SIMULATED WIND DATA FROM A MESOSCALE MODEL IN MCP. M. Taylor J. Freedman K. Waight M. Brower USING SIMULATED WIND DATA FROM A MESOSCALE MODEL IN MCP M. Taylor J. Freedman K. Waight M. Brower Page 2 ABSTRACT Since field measurement campaigns for proposed wind projects typically last no more than

More information

VALIDATION OF SAFNWC / MSG CLOUD PRODUCTS WITH ONE YEAR OF SEVIRI DATA

VALIDATION OF SAFNWC / MSG CLOUD PRODUCTS WITH ONE YEAR OF SEVIRI DATA VALIDATION OF SAFNWC / MSG CLOUD PRODUCTS WITH ONE YEAR OF SEVIRI DATA M.Derrien 1, H.Le Gléau 1, Jean-François Daloze 2, Martial Haeffelin 2 1 Météo-France / DP / Centre de Météorologie Spatiale. BP 50747.

More information

ENERGY YIELD PREDICTION OF AMORPHOUS SILICON PV MODULES USING FULL TIME DATA SERIES OF IRRADIANCE AND TEMPERATURE FOR DIFFERENT GEOGRAPHICAL LOCATIONS

ENERGY YIELD PREDICTION OF AMORPHOUS SILICON PV MODULES USING FULL TIME DATA SERIES OF IRRADIANCE AND TEMPERATURE FOR DIFFERENT GEOGRAPHICAL LOCATIONS Skoczek A., Virtuani A., Cebecauer T., Chianese D., 2011. Energy Yield Prediction of Amorphous Silicon PV Modules Using Full Time Data Series of Irradiance And Temperature for Different Geographical Locations.

More information

Solar Resource Assessment

Solar Resource Assessment Introduction to Resource Assessments Carsten Hoyer-Klick Folie 1 Solar Resource Assessment Folie 2 1 Global Horizontal Irradiation (GHI) Direct Horizontal Irradiation (DHI) Diffuse Irradiation (DIF) GHI

More information

IBM Big Green Innovations Environmental R&D and Services

IBM Big Green Innovations Environmental R&D and Services IBM Big Green Innovations Environmental R&D and Services Smart Weather Modelling Local Area Precision Forecasting for Weather-Sensitive Business Operations (e.g. Smart Grids) Lloyd A. Treinish Project

More information

Guidelines on Quality Control Procedures for Data from Automatic Weather Stations

Guidelines on Quality Control Procedures for Data from Automatic Weather Stations WORLD METEOROLOGICAL ORGANIZATION COMMISSION FOR BASIC SYSTEMS OPEN PROGRAMME AREA GROUP ON INTEGRATED OBSERVING SYSTEMS EXPERT TEAM ON REQUIREMENTS FOR DATA FROM AUTOMATIC WEATHER STATIONS Third Session

More information

Cloud detection and clearing for the MOPITT instrument

Cloud detection and clearing for the MOPITT instrument Cloud detection and clearing for the MOPITT instrument Juying Warner, John Gille, David P. Edwards and Paul Bailey National Center for Atmospheric Research, Boulder, Colorado ABSTRACT The Measurement Of

More information

The role of Earth Observation Satellites to observe rainfall. Riko Oki National Space Development Agency of Japan

The role of Earth Observation Satellites to observe rainfall. Riko Oki National Space Development Agency of Japan The role of Earth Observation Satellites to observe rainfall Riko Oki National Space Development Agency of Japan Outline 1. Importance of rain measurement 2. TRMM and Its Achievements 3. Outline of GPM

More information

Geographic smoothing of solar PV: Results from Gujarat. AMS 2016 Kelly Klima, Jay Apt

Geographic smoothing of solar PV: Results from Gujarat. AMS 2016 Kelly Klima, Jay Apt Geographic smoothing of solar PV: Results from Gujarat AMS 2016 Kelly Klima, Jay Apt 1 Many forms of renewable energy exist. Some are variable, requiring smoothing. Wind Solar Biomass Geothermal Wave Hydropower

More information

Partnership to Improve Solar Power Forecasting

Partnership to Improve Solar Power Forecasting Partnership to Improve Solar Power Forecasting Venue: EUPVSEC, Paris France Presenter: Dr. Manajit Sengupta Date: October 1 st 2013 NREL is a national laboratory of the U.S. Department of Energy, Office

More information

CSI EPBB Design Factor Calculator User Guide

CSI EPBB Design Factor Calculator User Guide CSI EPBB Design Factor Calculator User Guide 1. Guide Overview This User Guide is intended to provide background on the California Solar Initiative (CSI) Expected Performance Based Buydown (EPBB) Design

More information

Name of research institute or organization: École Polytechnique Fédérale de Lausanne (EPFL)

Name of research institute or organization: École Polytechnique Fédérale de Lausanne (EPFL) Name of research institute or organization: École Polytechnique Fédérale de Lausanne (EPFL) Title of project: Study of atmospheric ozone by a LIDAR Project leader and team: Dr. Valentin Simeonov, project

More information

Power Output Analysis of Photovoltaic Systems in San Diego County Mohammad Jamaly, Juan L Bosch, Jan Kleissl

Power Output Analysis of Photovoltaic Systems in San Diego County Mohammad Jamaly, Juan L Bosch, Jan Kleissl 1 Power Output Analysis of Photovoltaic Systems in San Diego County Mohammad Jamaly, Juan L Bosch, Jan Kleissl Abstract Aggregate ramp rates of 86 distributed photovoltaic (PV) systems installed in Southern

More information

The impact of window size on AMV

The impact of window size on AMV The impact of window size on AMV E. H. Sohn 1 and R. Borde 2 KMA 1 and EUMETSAT 2 Abstract Target size determination is subjective not only for tracking the vector but also AMV results. Smaller target

More information

Extreme Events in the Atmosphere

Extreme Events in the Atmosphere Cover Extreme Events in the Atmosphere Basic concepts Academic year 2013-2014 ICTP Trieste - Italy Dario B. Giaiotti and Fulvio Stel 1 Outline of the lecture Definition of extreme weather event. It is

More information

A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30- Year Actual Weather Data

A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30- Year Actual Weather Data LBNL-6280E ERNEST ORLANDO LAWRENCE BERKELEY NATIONAL LABORATORY A Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30- Year Actual Weather Data Tianzhen Hong 1,

More information

Preliminary advances in Climate Risk Management in China Meteorological Administration

Preliminary advances in Climate Risk Management in China Meteorological Administration Preliminary advances in Climate Risk Management in China Meteorological Administration Gao Ge Guayaquil,Ecuador, Oct.2011 Contents China Framework of Climate Service Experience in Climate/disaster risk

More information

Solar Tracking Application

Solar Tracking Application Solar Tracking Application A Rockwell Automation White Paper Solar trackers are devices used to orient photovoltaic panels, reflectors, lenses or other optical devices toward the sun. Since the sun s position

More information

IMPROVING THE PERFORMANCE OF SATELLITE-TO-IRRADIANCE MODELS USING THE SATELLITE S INFRARED SENSORS

IMPROVING THE PERFORMANCE OF SATELLITE-TO-IRRADIANCE MODELS USING THE SATELLITE S INFRARED SENSORS IMPROVING THE PERFORMANCE OF SATELLITE-TO-IRRADIANCE MODELS USING THE SATELLITE S INFRARED SENSORS Richard Perez ASRC, Albany, NY, 12203 Perez@asrc.cestm.albany,edu Sergey Kivalov ASRC, Albany, NY, 12203

More information

Daily High-resolution Blended Analyses for Sea Surface Temperature

Daily High-resolution Blended Analyses for Sea Surface Temperature Daily High-resolution Blended Analyses for Sea Surface Temperature by Richard W. Reynolds 1, Thomas M. Smith 2, Chunying Liu 1, Dudley B. Chelton 3, Kenneth S. Casey 4, and Michael G. Schlax 3 1 NOAA National

More information

Near Real Time Blended Surface Winds

Near Real Time Blended Surface Winds Near Real Time Blended Surface Winds I. Summary To enhance the spatial and temporal resolutions of surface wind, the remotely sensed retrievals are blended to the operational ECMWF wind analyses over the

More information

Financing Community Wind

Financing Community Wind Financing Community Wind Wind Data and Due Diligence What is the Project's Capacity Factor? Community Wind Energy 2006 March 8, 2006 Mark Ahlstrom mark@windlogics.com Slide 1 The Need for Wind Assessment

More information

Solar Radiation Measurement. Bruce W Forgan, WMO RAV Metrology Workshop, Melbourne, Novemberr 2011

Solar Radiation Measurement. Bruce W Forgan, WMO RAV Metrology Workshop, Melbourne, Novemberr 2011 Solar Radiation Measurement Bruce W Forgan, WMO RAV Metrology Workshop, Melbourne, Novemberr 2011 Why Do We Need Data on Solar Energy? Global Climate System Climate Energy Balance Solar Exposure and Irradiance

More information

The Wind Integration National Dataset (WIND) toolkit

The Wind Integration National Dataset (WIND) toolkit The Wind Integration National Dataset (WIND) toolkit EWEA Wind Power Forecasting Workshop, Rotterdam December 3, 2013 Caroline Draxl NREL/PR-5000-60977 NREL is a national laboratory of the U.S. Department

More information

APPENDIX A. Bay Area Air Quality Management District

APPENDIX A. Bay Area Air Quality Management District APPENDIX A Bay Area Air Quality Management District Meteorological Monitoring Guidance for Manual of Procedures, Volume VI: Air Monitoring Procedures (Adopted July 20, 1994) (Latest Revision March 21,

More information

Technology Advantage

Technology Advantage Technology Advantage 2 FIRST SOLAR TECHNOLOGY ADVANTAGE 3 The Technology Advantage Cadmium Telluride (CdTe) photovoltaic (PV) technology continues to set performance records in both research and real-world

More information

The FAA Aviation Weather Research Program Quality Assessment Product Development Team

The FAA Aviation Weather Research Program Quality Assessment Product Development Team The FAA Aviation Weather Research Program Quality Assessment Product Development Team Jennifer Luppens Mahoney NOAA Research-Earth System Research Laboratory/Global Systems Division Barbara Brown National

More information

2012. American Solar Energy Society Proc. ASES Annual Conference, Raleigh, NC.

2012. American Solar Energy Society Proc. ASES Annual Conference, Raleigh, NC. 2012. American Solar Energy Society Proc. ASES Annual Conference, Raleigh, NC. PREDICTING SHORT-TERM VARIABILITY OF HIGH-PENETRATION PV Thomas E. Hoff Clean Power Research Napa, CA 94558 tomhoff@cleanpower.com

More information

Smart control and Big Data in PV

Smart control and Big Data in PV Copernicus Institute of Sustainable Development Smart control and Big Data in PV Wilfried van Sark Sunday 2015 18 November 2015 1/36 Contents Big data PV developments Example projects with Big Data Advanced

More information

The Role of Resource Assessment in Scaling Up Renewable Energy

The Role of Resource Assessment in Scaling Up Renewable Energy The Role of Resource Assessment in Scaling Up Renewable Energy Charging Ahead: Scaling Up Renewable Energy in the Developing World Nisha Thirumurthy October 27, 2015 NREL is a national laboratory of the

More information

Section 3 What Is Climate?

Section 3 What Is Climate? Section 3 What Is Climate? Key Concept Earth s climate zones are caused by the distribution of heat around Earth s surface by wind and ocean currents. What You Will Learn Climate is the average weather

More information

Forecasting of Solar Radiation

Forecasting of Solar Radiation Forecasting of Solar Radiation Detlev Heinemann, Elke Lorenz, Marco Girodo Oldenburg University, Institute of Physics, Energy and Semiconductor Research Laboratory, Energy Meteorology Group 26111 Oldenburg,

More information

Monitoring of Arctic Conditions from a Virtual Constellation of Synthetic Aperture Radar Satellites

Monitoring of Arctic Conditions from a Virtual Constellation of Synthetic Aperture Radar Satellites DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Monitoring of Arctic Conditions from a Virtual Constellation of Synthetic Aperture Radar Satellites Hans C. Graber RSMAS

More information

Running the Electric Meter Backwards: Real-Life Experience with a Residential Solar Power System

Running the Electric Meter Backwards: Real-Life Experience with a Residential Solar Power System Running the Electric Meter Backwards: Real-Life Experience with a Residential Solar Power System Brooks Martner Lafayette, Colorado University of Toledo Spring 2015 PHYS 4400 - Principles and Varieties

More information

OFF-GRID ELECTRICITY GENERATION WITH HYBRID RENEWABLE ENERGY TECHNOLOGIES IN IRAQ: AN APPLICATION OF HOMER

OFF-GRID ELECTRICITY GENERATION WITH HYBRID RENEWABLE ENERGY TECHNOLOGIES IN IRAQ: AN APPLICATION OF HOMER Diyala Journal of Engineering Sciences ISSN 1999-8716 Printed in Iraq Second Engineering Scientific Conference College of Engineering University of Diyala 16-17 December. 2015, pp. 277-286 OFF-GRID ELECTRICITY

More information

NEW US WIND ENERGY POTENTIAL ESTIMATES

NEW US WIND ENERGY POTENTIAL ESTIMATES NEW US WIND ENERGY POTENTIAL ESTIMATES Background and Explanation of Changes from Prior Estimates Michael Brower, CTO AWS Truepower, LLC 463 New Karner Road Albany, NY 12205 awstruepower.com info@awstruepower.com

More information

Data Processing Flow Chart

Data Processing Flow Chart Legend Start V1 V2 V3 Completed Version 2 Completion date Data Processing Flow Chart Data: Download a) AVHRR: 1981-1999 b) MODIS:2000-2010 c) SPOT : 1998-2002 No Progressing Started Did not start 03/12/12

More information

THE STRATEGIC PLAN OF THE HYDROMETEOROLOGICAL PREDICTION CENTER

THE STRATEGIC PLAN OF THE HYDROMETEOROLOGICAL PREDICTION CENTER THE STRATEGIC PLAN OF THE HYDROMETEOROLOGICAL PREDICTION CENTER FISCAL YEARS 2012 2016 INTRODUCTION Over the next ten years, the National Weather Service (NWS) of the National Oceanic and Atmospheric Administration

More information

VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR

VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR Andrew Goldstein Yale University 68 High Street New Haven, CT 06511 andrew.goldstein@yale.edu Alexander Thornton Shawn Kerrigan Locus Energy 657 Mission St.

More information

Huai-Min Zhang & NOAAGlobalTemp Team

Huai-Min Zhang & NOAAGlobalTemp Team Improving Global Observations for Climate Change Monitoring using Global Surface Temperature (& beyond) Huai-Min Zhang & NOAAGlobalTemp Team NOAA National Centers for Environmental Information (NCEI) [formerly:

More information

PERFORMANCE EVALUATION OF SOLAR ENERGY DEVICES BY USING A DATA LOGGING SYSTEM

PERFORMANCE EVALUATION OF SOLAR ENERGY DEVICES BY USING A DATA LOGGING SYSTEM PERFORMANCE EVALUATION OF SOLAR ENERGY DEVICES BY USING A DATA LOGGING SYSTEM Vijay Singh and Mohd. Murtaja Department of Electronics and Instrumentation Engineering Ch. Charan Singh University Campus,

More information

Progress on an Updated National Solar Radiation Data Base

Progress on an Updated National Solar Radiation Data Base March 2004 NREL/CP-560-36038 Progress on an Updated National Solar Radiation Data Base Preprint S. Wilcox, M. Anderberg, R. George, W. Marion, D. Myers, and D. Renne National Renewable Energy Laboratory

More information

Virtual Met Mast verification report:

Virtual Met Mast verification report: Virtual Met Mast verification report: June 2013 1 Authors: Alasdair Skea Karen Walter Dr Clive Wilson Leo Hume-Wright 2 Table of contents Executive summary... 4 1. Introduction... 6 2. Verification process...

More information

DATA STORAGE SYSTEM FOR LS7001/LS8000 LIGHTNING DETECTION NETWORKS

DATA STORAGE SYSTEM FOR LS7001/LS8000 LIGHTNING DETECTION NETWORKS DATA STORAGE SYSTEM FOR LS7001/LS8000 LIGHTNING DETECTION NETWORKS J. López 1,2, M. Murphy 3, M. Maruri 1,2, D. de la Vega 4, J.A. Aranda 5, S. Gaztelumendi 1,2 1 Basque Meteorology Agency (EUSKALMET),

More information

USE OF REMOTE SENSING FOR WIND ENERGY ASSESSMENTS

USE OF REMOTE SENSING FOR WIND ENERGY ASSESSMENTS RECOMMENDED PRACTICE DNV-RP-J101 USE OF REMOTE SENSING FOR WIND ENERGY ASSESSMENTS APRIL 2011 FOREWORD (DNV) is an autonomous and independent foundation with the objectives of safeguarding life, property

More information

SOLAR RADIATION AND YIELD. Alessandro Massi Pavan

SOLAR RADIATION AND YIELD. Alessandro Massi Pavan SOLAR RADIATION AND YIELD Alessandro Massi Pavan Sesto Val Pusteria June 22 nd 26 th, 2015 DEFINITIONS Solar radiation: general meaning Irradiation [Wh/m 2 ]: energy received per unit area Irradiance [W/m

More information

Simply follow the sun. up to 0.0003. www.siemens.com/solar-industry. Maximize the yields of solar power plants with solar tracking control

Simply follow the sun. up to 0.0003. www.siemens.com/solar-industry. Maximize the yields of solar power plants with solar tracking control up to 0.0003 accuracy with an astronomical algorithm and SIMATIC S7-1200 www.siemens.com/solar-industry Simply follow the sun Maximize the yields of solar power plants with solar tracking control Answers

More information

5.5. San Diego (8/22/03 10/4/04)

5.5. San Diego (8/22/03 10/4/04) NSF UV SPECTRORADIOMETER NETWORK 23-24 OPERATIONS REPORT 5.5. San Diego (8/22/3 1/4/4) The 23-24 season at San Diego includes the period 8/22/3 1/4/4. In contrast to other network sites, San Diego serves

More information

Global Seasonal Phase Lag between Solar Heating and Surface Temperature

Global Seasonal Phase Lag between Solar Heating and Surface Temperature Global Seasonal Phase Lag between Solar Heating and Surface Temperature Summer REU Program Professor Tom Witten By Abstract There is a seasonal phase lag between solar heating from the sun and the surface

More information

A study on major design elements of tracking-type floating photovoltaic systems

A study on major design elements of tracking-type floating photovoltaic systems International Journal of Smart Grid and Clean Energy A study on major design elements of tracking-type floating photovoltaic systems Young-Kwan Choi a*, Nam-Hyung Lee a, An-Kyu Lee a, Kern-Joong Kim b

More information

Adjustment of Anemometer Readings for Energy Production Estimates WINDPOWER June 2008 Houston, Texas

Adjustment of Anemometer Readings for Energy Production Estimates WINDPOWER June 2008 Houston, Texas Adjustment of Anemometer Readings for Energy Production Estimates WINDPOWER June 2008 Houston, Texas Matthew Filippelli, Julien Bouget, Michael Brower, and Dan Bernadett AWS Truewind, LLC 463 New Karner

More information

Comprehensive Forecasting System for Variable Renewable Energy

Comprehensive Forecasting System for Variable Renewable Energy Branko Kosović Sue Ellen Haupt, Gerry Wiener, Luca Delle Monache, Yubao Liu, Marcia Politovich, Jenny Sun, John Williams*, Daniel Adriaansen, Stefano Alessandrini, Susan Dettling, and Seth Linden (NCAR,

More information

The Cost of Solar Power Variability

The Cost of Solar Power Variability The Cost of Solar Power Variability Abstract We compare the power spectra of a year of electricity generation data from the Nevada Solar One solar thermal plant, a Tucson Electric Power solar PV array,

More information

Solar energy... ...Solar technology in endurance testing

Solar energy... ...Solar technology in endurance testing Solar energy......solar technology in endurance testing Solar & Photovoltaic systems - Environmental testing...... we are your partner Energy generated by the sun (solar energy) is considered forward looking

More information

Predicting Solar Power Production:

Predicting Solar Power Production: $1,995 Non-Member Price Free for members only Predicting Solar Power Production: Irradiance Forecasting Models, Applications and Future Prospects Steven Letendre, PhD Miriam Makhyoun Mike Taylor Green

More information

Power Prediction Analysis using Artificial Neural Network in MS Excel

Power Prediction Analysis using Artificial Neural Network in MS Excel Power Prediction Analysis using Artificial Neural Network in MS Excel NURHASHINMAH MAHAMAD, MUHAMAD KAMAL B. MOHAMMED AMIN Electronic System Engineering Department Malaysia Japan International Institute

More information

Stormwater Management in a Changing Climate: Current Initiatives at TRCA

Stormwater Management in a Changing Climate: Current Initiatives at TRCA Stormwater Management in a Changing Climate: Current Initiatives at TRCA Ryan Ness Toronto and Region Conservation Authority A.D. Latornell Conservation Symposium November 21, 2008 TRCA Jurisdiction History

More information

USING CLOUD CLASSIFICATION TO MODEL SOLAR VARIABILITY

USING CLOUD CLASSIFICATION TO MODEL SOLAR VARIABILITY USING CLOUD CLASSIFICATION TO MODEL SOLAR VARIABILITY Matthew J. Reno Sandia National Laboratories Georgia Institute of Technology 777 Atlantic Drive NW Atlanta, GA 3332-25, USA matthew.reno@gatech.edu

More information

Small PV Systems Performance Evaluation at NREL's Outdoor Test Facility Using the PVUSA Power Rating Method

Small PV Systems Performance Evaluation at NREL's Outdoor Test Facility Using the PVUSA Power Rating Method National Renewable Energy Laboratory Innovation for Our Energy Future A national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Small PV Systems Performance

More information

Improving Accuracy of Solar Forecasting February 14, 2013

Improving Accuracy of Solar Forecasting February 14, 2013 Improving Accuracy of Solar Forecasting February 14, 2013 Solar Resource Forecasting Objectives: Improve accuracy of solar resource forecasts Enable widespread use of solar forecasts in power system operations

More information

Developing sub-domain verification methods based on Geographic Information System (GIS) tools

Developing sub-domain verification methods based on Geographic Information System (GIS) tools APPROVED FOR PUBLIC RELEASE: DISTRIBUTION UNLIMITED U.S. Army Research, Development and Engineering Command Developing sub-domain verification methods based on Geographic Information System (GIS) tools

More information