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01_​Energy_​Usage_​Time_​Series_​Prediction

Time Series Prediction

This workflow builds an auto-regressive model to predict energy usage. The first week of the time series is used as a template for seasonality correction: the data are differenced by subtracting the values in the same hour in the previous week from the current values. Only past time series are used for prediction. No other external time series/data used. The regression model can be either a linear or a polynomial regression model.

Change lag here! Auto-regressive model to predict hourly energy usage. linear AR(lag)add templateback intopredictions7*24h seasonality correctionx(t) = x(t) - x(t-7*24)Here is where the time series gets selected andrenamed to "cluster"x(t) and x(t-7*24)from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)Original vs.Predicted Linear Regression re-build signal Math Formula Prepare Data Lag Column Lag Column File Reader Line Plot (Plotly) Change lag here! Auto-regressive model to predict hourly energy usage. linear AR(lag)add templateback intopredictions7*24h seasonality correctionx(t) = x(t) - x(t-7*24)Here is where the time series gets selected andrenamed to "cluster"x(t) and x(t-7*24)from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)Original vs.PredictedLinear Regression re-build signal Math Formula Prepare Data Lag Column Lag Column File Reader Line Plot (Plotly)

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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: 2024-02-20
Last update: 2024-11-23
Created with KNIME version: v4.1.0
Tags: Time SeriesEnergy UsageIoTInternet of ThingsDemand prediction

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