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Irradiance LSTM Forecast Model

Time SeriesPredictionLSTMDeep LearningNeural Network
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carlosenrique84 profile image
Version2.0Latest, created on 
Jul 10, 2024 9:16 PM
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GLOBAL HORIZONTAL IRRADIANCE LSTM FORECAST MODEL

The University of Saskatchewan
Ph.D. in Interdisciplinary Studies

Created by: Carlos Enrique Diaz, MBM, P.Eng.
Email: carlos.diaz@usask.ca

Supervisor: Lori Bradford, Ph.D.
Email: lori.bradford@usask.ca

This workflow forecasts time series (irradiance) using an LSTM network with lagged values as input. The trained model is then used for out-of-sample forecasting. The predicted values are compared to the actual values, and the forecast's performance is reported via scoring metrics and a line plot in a dashboard. This workflow used GHI data from CWEEDS for Saskatoon and was adapted from the following KNIME workflow:

https://hub.knime.com/-/spaces/-/~Qo3OuYyVQV58k9cq/

External resources

  • CWEEDS Data
  • KNIME Workflow Template
  • "Once Upon A Time..." by LSTM Network
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Created with KNIME Analytics Platform version 5.2.4
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