Evaluate Naive Models for Forecasting 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.

In this tutorial, you will discover how to develop a test harness for the ‘household power consumption’ dataset and evaluate three naive forecast strategies that provide a baseline for more sophisticated algorithms.

After completing this tutorial, you will know:

• How to load, prepare, and downsample the household power consumption dataset ready for developing models.
• How to develop metrics, dataset split, and walk-forward validation elements for a robust test harness for evaluating forecasting models.
• How to develop and evaluate and compare the performance a suite of naive persistence forecasting methods.

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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