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02_​TSA_​with_​LSTM_​Network_​Deployment

Energy Demand Prediction with LSTM - Deployment

This workflow applies an LSTM network to predict energy demand using lagged values of a time series as input.






Data Loading Data Cleaning Time Series Analysis Demand Prediction LSTM Network - DeploymentIn this workflow we deploy a traned LSTM Network to predict the next n samples in energy data. Create Input Vector Trained Network Predicting n next values convertdate/timeinto Date&Time objectscombine intolistjust Listlag 200 valuesout-sample predictionsget latestsort in timeenergy deployment dataIntroducemissingdate times Missing Value String to Date&Time Column Filter Column Aggregator Column Filter Lag Column Deployment Loop Keras NetworkReader Row Filter Sorter Table Reader Line Plot (Plotly) Timestamp Alignment Data Loading Data Cleaning Time Series Analysis Demand Prediction LSTM Network - DeploymentIn this workflow we deploy a traned LSTM Network to predict the next n samples in energy data. Create Input Vector Trained Network Predicting n next values convertdate/timeinto Date&Time objectscombine intolistjust Listlag 200 valuesout-sample predictionsget latestsort in timeenergy deployment dataIntroducemissingdate times Missing Value String to Date&Time Column Filter Column Aggregator Column Filter Lag Column Deployment Loop Keras NetworkReader Row Filter Sorter Table Reader Line Plot (Plotly) Timestamp Alignment

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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On NodePit since: 2020-12-17
Last update: 2024-04-26
Created with KNIME version: v4.2.3
Tags: Time SeriesPredictionEnergy UsageLSTMDeep LearningNeural NetworkKeras

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