Multivariate Irradiance LSTM Forecast Model - Deployment

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The University of Saskatchewan
Ph.D. in Interdisciplinary Studies

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

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

This is a simple example workflow for multivariant time series analysis using an LSTM based recurrent neural network and implemented via the KNIME Deep Learning - Keras Integration.

This workflow used GHI data from CWEEDS for Saskatoon and trains a model to predict the Global Horizontal Irradiance (GHI) in the next hour based on the values in the last 10 hours. The workflow was adapted from the following workflow:

https://hub.knime.com/-/spaces/-/~acxQcWj9lyHLyyrh/

URL: Workflow Template https://hub.knime.com/-/spaces/-/~mPu4tSYH6TPDFHVa/
URL: CWEEDS Data https://climate.weather.gc.ca/prods_servs/engineering_e.html
URL: Multivariate Time Series Analysis: LSTMs & Codeless https://www.knime.com/blog/multivariate-time-series-analysis-lstm-codeless

Nodes

• Column Filter2 ×
• CSV Reader1 ×
• Column Expressions1 ×
• Column List Loop Start1 ×
• Extract Column Header1 ×
• Column Renamer1 ×
• Counter Generation1 ×
• Create Collection Column1 ×
• Denormalizer1 ×
• Date&Time Part Extractor1 ×
• Keras Network Executor1 ×
• Keras Network Reader1 ×
• Lag Column1 ×
• Loop End (Column Append)1 ×
• Math Formula1 ×
• Model Reader1 ×
• Normalizer (Apply)1 ×
• Number To String1 ×
• Reference Column Resorter1 ×
• Row Filter1 ×
• Sorter1 ×
• String to Date&Time1 ×
• Transpose1 ×

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Column Expressions (Labs)
• FeatureKNIME Data Generation
• FeatureKNIME Deep Learning - Keras Integration
• FeatureKNIME Math Expression (JEP)

Links

• No links available