Autoregression Forecast Model for Household Electricity Consumption

Last Updated on August 28, 2020

Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available.

This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption.

Autocorrelation models are very simple and can provide a fast and effective way to make skillful one-step and multi-step forecasts for electricity consumption.

In this tutorial, you will discover how to develop and evaluate an autoregression model for multi-step forecasting household power consumption.

After completing this tutorial, you will know:

• How to create and analyze autocorrelation and partial autocorrelation plots for univariate time series data.
• How to use the findings from autocorrelation plots to configure an autoregression model.
• How to develop and evaluate an autocorrelation model used to make one-week forecasts.

Kick-start your project with my new book Deep Learning for Time Series Forecasting, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

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