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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.

URL: Energy Usage Prediction (Time Series Prediction) https://www.knime.org/knime-applications/energy-usage-prediction
URL: All you need is ... the Lag Column Node! https://www.knime.com/blog/all-you-need-is-the-lag-column-node
URL: The Lag Column Node: The Key to Time Series Analysis https://youtu.be/pR_7pIEqW-c

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

Nodes

Extensions

Links

Download

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

Download Workflow
On NodePit since: 2025-11-23
Last update: 2025-11-24
Created with KNIME version: v5.8.0
Tags: Time SeriesEnergy UsageIoTInternet of ThingsDemand prediction

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