Litcius/Paper detail

A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism

Zahra Fazlipour, Elaheh Mashhour, Mahmood Joorabian

2022Applied Energy84 citationsDOI

Topics & Concepts

AutoencoderComputer scienceRobustness (evolution)Artificial intelligenceUnivariateDeep learningMachine learningArtificial neural networkConditional random fieldSequence (biology)Data miningMultivariate statisticsChemistryBiochemistryGeneticsBiologyGeneEnergy Load and Power ForecastingGrey System Theory ApplicationsStock Market Forecasting Methods
A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism | Litcius