Demand Response For Residential Uses: A Data Analytics Approach

TL;DR

This paper presents a data analytics framework for residential demand response in smart grids, utilizing appliance usage detection and mode recognition to promote energy-saving behaviors during off-peak hours.

Contribution

It introduces a novel system combining cross correlation and dynamic time warping techniques for appliance detection and mode recognition in residential energy consumption data.

Findings

Effective detection of appliance usage times.

Accurate recognition of operation modes.

Potential to encourage energy-saving behaviors.

Abstract

In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system foundation that is applied to user's disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation (XCORR) to detect times of the occurrences when an appliance is being used. We then use The Dynamic Time Warping (DTW) to recognize the operation mode used.