2025AI人工智能精準功率預測提升發(fā)電效率

AI人工智能精準天氣預報

提升海上風電運營效率

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01 StormGeo海上風電服務概述

內(nèi)容概要

AI天氣預報簡介

案例分析：AI預測在實際應用中的表現(xiàn)

StormGeoDELFI模型

AI預測模型的未來發(fā)展

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StormGeo海上風電服務概述

StormGeo–全球天氣敏感行業(yè)的氣象服務提供商

1998年成立于挪威卑爾根

全球25個辦事處和600名員工

航運、石油和天然氣、可再生能源、跨行業(yè)、媒體

24/7/365

阿法拉伐集團旗下全資子公司

StormGeo在海上風電領域的服務

全球超過200個項目業(yè)績

項目分布歐洲、亞洲、南北美洲

精準的短期和長期天氣預報

雷電、熱帶風暴預警

定制化的氣象數(shù)據(jù)

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提供決策支持

海上風電作業(yè)中的氣象風險

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StormGeo氣象預報服務平臺

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定點作業(yè)風險評估

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定點未來7天精準的小時預報

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雷電預警

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TyphoonYagiNo.11,2024

提前7天臺風預警

平臺實時顯示最新熱帶氣旋信息

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提前7天臺風預警

每天不間斷發(fā)布熱帶氣旋發(fā)展情況詳細介紹以及對客戶項目的影響

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AI天氣預報簡介

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Forecastingweather

MultipleweathermodelsgivealternativepredictionsforeachsiteForecasterscanmanuallycorrectforecaststoimproveaccuracy

Notethatnotallworkismanual-simplemethodsforprecorrectionsofforecastsexistsalready

WhatisAIforecasting?

Weatherpredictionfromdatainsteadof(only)numerical/physicalequations

Numericalweathermodel

AIglobalmodel(AIdata-basedmodel)

Basedonphysicalequationsdescribingtheatmosphericprocesses

Trainedtorecognisepatternsinweatherbehaviourbasedon40yearshistoricalarchiveofglobalweatherre-analysis

GlobalweatherforecastingwithAI

Method:Learnfromweatherdata-identifyingpatternsandtrendsthathumanforecastersortraditionalmodelsmightoverlook

Trainingon40yearshistoricaldataarchive(ERA5)

AninterativeprocessContinouslearning

Timestep(6hours) Timestep(6hours)

Identifypatternsinhowweathervariablesdevelopstimestepbytimestep

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Source:WeatherforecastingintheageofAI()

(modified)

Howdothemodelsperform?

AImodelsperforms“topoftheclass”

WellknownAImodels:

AIFS

PanguFourcastnetGraphcastFuxi

--ECAImodel

OtherAImodels

ECMWFclassicmodel

Otherclassicweathermodels

TalkbyLangatECMWFUEF24

案例分析：AI預測在實際應用中的表現(xiàn)

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s

Case1:Finlandtemperature

AImodelcatchescoldanomalymuchearlierthantheclassicalmodel

Classicalmodel

AImodelObservation

BenBouallegueetal,2024 17

Case2:Cycloneintensity

Performswellforcyclonetracksbutnotthecycloneintensity

\h

Source:MarianaClare,ECMWFIEAWindTask51DeepLearningWeatherPredictionWebinar,11Jan2024()

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Case3:Simulatingunprecedentedweather

CasestudyofthestrongeststormeverobservedoverUKStormCieranfall2023

Classicmodel

AImodel

AstormofcomparablecentralpressurehasneverpreviouslybeenobservedoverEngland

AImodelsaretrainedondataYettheywereablesimulatethelarge-scaledevelopmentofthestorm

Maybebecauseprevious(weaker)weatherdevelopmentseeninERA5data

Charlton-Perezetal,2024 19

OpinionsonAIfromaforecaster

Summaryfromaforecastperformanceviewpoint

Strengths:

SuperiorRMSEforz500

Mainlysuperioroverocean(?)

Moreconsistentfromforecasttoforecast(less”jumpy”)Largescalefeaturesofextremeswellpredicted

LackingmesoscalestructuresVeryimpressivetropicalcyclonetrackscores

Weaknesses:

Smoothfieldsofe.gprecipitationTooweaktropicalcyclones

IstheAImodelslackingsomeofthechaoticnatureofweather?

Developmentstillmissing:

Currentlynotdirectlyimpactparameterslikeclouds(andsomemodelsprecipitation)Ensemblesystems(NB:nowrecentlyreleased)

SeetalkonAIforecastevaluationbyLinus

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Magnussion,ECMWF,UEF2024ForecastevaluationofAIFSonVimeo

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Data-drivenforecasting

AIFSuseanalysis–canwelearnfromobservations(alone)?

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StormGeoDELFI模型

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WhatisDELFI

DELFIisapoint-basedmachinelearningmodelanddiffersfromclassicalweatherforecastmodels

ECMWF

DELFI

Observations

ECMWFforecast

DELFIcorrectedforecast

HowdoesDELFIwork?

Machinelearning-basedonhistoricalcomparisonsofforecastsandobservations

ECMWF

ECMWF

(orothermodel)forecast

Observations(fromclients)

Historicalarchive

Improvedforecastwithleasterror

DELFI:

Machinelearningalgorithms

Trainedonthehistoricalarchiveofdata

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In-DepthlookatDELFI’sMachineLearningComponents

Severalmachinelearningalgorithmsarecontinouslytestedagainstforecast

ECMWF

(orothermodel)forecast

Observations

Historicalarchive

IfDELFIdoesnotimprovetheforecast,thennoautomaticcorrectionisapplied

DELFI:

Machinelearningalgorithms

Trainedonthehistoricalarchiveofdata

LinearregressionRandomforestTikhonovRegularizationWeibulldistributionHybridmodel

Algorithm1Algorithm2Algorithm3

Selection

Forecastwithleasterror

DELFI

ApointspecificcorrecteddatasourcedeliveredthroughWOD

AvailableinFPSassupportcurveandPrecorrectiontoassistforecasters

DELFI

monitor

AI預測模型的未來發(fā)展

Differentfocusareasthatyieldresultsinshorte