2025AI人工智能精准功率预测提升发电效率

AI人工智能精准天气预报

提升海上风电运营效率

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01 StormGeo海上风电服务概述

内容概要

AI天气预报简介

案例分析：AI预测在实际应用中的表现

StormGeoDELFI模型

AI预测模型的未来发展

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StormGeo海上风电服务概述

StormGeo–全球天气敏感行业的气象服务提供商

1998年成立于挪威卑尔根

全球25个办事处和600名员工

航运、石油和天然气、可再生能源、跨行业、媒体

24/7/365

阿法拉伐集团旗下全资子公司

StormGeo在海上风电领域的服务

全球超过200个项目业绩

项目分布欧洲、亚洲、南北美洲

精准的短期和长期天气预报

雷电、热带风暴预警

定制化的气象数据

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提供决策支持

海上风电作业中的气象风险

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StormGeo气象预报服务平台

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定点作业风险评估

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定点未来7天精准的小时预报

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雷电预警

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TyphoonYagiNo.11,2024

提前7天台风预警

平台实时显示最新热带气旋信息

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提前7天台风预警

每天不间断发布热带气旋发展情况详细介绍以及对客户项目的影响

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AI天气预报简介

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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

\h

Source:WeatherforecastingintheageofAI()

(modified)

Howdothemodelsperform?

AImodelsperforms“topoftheclass”

WellknownAImodels:

AIFS

PanguFourcastnetGraphcastFuxi

--ECAImodel

OtherAImodels

ECMWFclassicmodel

Otherclassicweathermodels

TalkbyLangatECMWFUEF24

案例分析：AI预测在实际应用中的表现

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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预测模型的未来发展

Differentfocusareasthatyieldresultsinshorte