语言模型：预测、可解释性及其他.pdf

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1、Transformer Based Time-Series Language Model for Energy ForecastingArtificial Intelligence(AI)and Digital Transformation(DX)Electric Power SummitDr.Satish Natti&Dr.Parul Arora01/07/2025www.gridaxon.io 1Contents Introduction Model Framework Case Study Dataset Description Results Comparison Conclusion

2、1/7/25www.gridaxon.io 2Introduction1/7/25www.gridaxon.io 3CustomersProject DevelopersIPPsPower MarketersISOs/UtilitiesService AreasSite IdentificationInterconnection ProcessGrid ModernizationNERC ComplianceExperience70+years of Cumulative experience30+GW projects across all marketsSite identificatio

3、n to Structuring PPAs AnalysisFeasibility StudiesSystem Impact StudiesEconomic StudiesModeling PSSe/PSLF/ASPEN/PSCAD/PROMOD/TARAIntroduction1/7/25www.gridaxon.io 4Key Idea Use of language models like T5 and GPT-2 for probabilistic time-series energy forecasting Motivation Time series tasks lack a ge

4、neral-purpose,pretrained framework like those in NLPChallenges Limited availability of high-quality time series datasets compared to text Existing forecasting models require dataset-specific fine-tuning and are resource-intensiveObjective Develop a unified,pretrained framework for time series with z

5、ero-shot and probabilistic forecasting capabilities To develop a model that is valuable in situations where historical time-series data is unavailable or when quick results are needed without extensive pre-trainingIntroduction Need for Energy Forecasting Balance supply and demand Reliable power deli

6、very by balancing generation to consumption Cost optimization Optimal resource allocation to lower the costs Renewable energy integration Manage intermittency and curtailment Energy markets Improve trading efficiency and reduce financial risk1/7/25www.gridaxon.io 5Model Framework A time series is tr