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Energy Consumption Forecasting with LSTM

This workflow predicts time series (energy consumption) by an LSTM network with lagged values as input. The trained model is then used for out-of-sample forecasting. The forecasted values are compared to the actual signal values, and the performance of the forecast is reported via scoring metrics and a line plot.





Data Loading Data Cleaning Network Architecture Create Input Vector Generate Forecasts in Loop convertdate/timeinto Date&Time objectsIntroducemissinghourstraining withMSE loss function[200] tensor for200 lagged inputs80/20 split[1] tensor foroutputLSTM with 100units and ReLUcombine intolistlag 200 valuesEnergyusagedata Missing Value String to Date&Time Column Filter Timestamp Alignment Line Plot Keras NetworkLearner Keras Input Layer Partitioning Keras Dense Layer Numeric Scorer Keras LSTM Layer Column Aggregator Lag Column Deployment Loop File Reader Data Loading Data Cleaning Network Architecture Create Input Vector Generate Forecasts in Loop convertdate/timeinto Date&Time objectsIntroducemissinghourstraining withMSE loss function[200] tensor for200 lagged inputs80/20 split[1] tensor foroutputLSTM with 100units and ReLUcombine intolistlag 200 valuesEnergyusagedata Missing Value String to Date&Time Column Filter Timestamp Alignment Line Plot Keras NetworkLearner Keras Input Layer Partitioning Keras Dense Layer Numeric Scorer Keras LSTM Layer Column Aggregator Lag Column Deployment Loop File Reader

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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: 2021-11-17
Last update: 2026-01-06
Created with KNIME version: v4.6.3
Tags: Time SeriesPredictionEnergy UsageLSTMDeep LearningNeural NetworkKeras

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