An Energy Activity Dataset for Smart Homes

TL;DR

This paper introduces a comprehensive energy activity dataset for smart homes, along with novel algorithms for data similarity measurement and a CNN-based OCR method for direct energy data acquisition.

Contribution

It provides a diverse, labeled energy dataset and proposes new algorithms for dataset similarity and non-intrusive energy data extraction.

Findings

Energy consumption patterns vary across appliances.

The LCS-based similarity algorithm effectively compares datasets.

The SCNN approach accurately extracts energy data from monitors.

Abstract

A smart home energy dataset that records miscellaneous energy consumption data is publicly offered. The proposed energy activity dataset (EAD) has a high data type diversity in contrast to existing load monitoring datasets. In EAD, a simple data point is labeled with the appliance, brand, and event information, whereas a complex data point has an extra application label. Several discoveries have been made on the energy consumption patterns of many appliances. Load curves of the appliances are measured when different events and applications are triggered and utilized. A revised longest-common-subsequence (LCS) similarity measurement algorithm is proposed to calculate energy dataset similarities. Thus, the data quality prior information becomes available before training machine learning models. In addition, a subsample convolutional neural network (SCNN) is put forward. It serves as a non-intrusive optical character recognition (OCR) approach to obtain energy data directly from monitors of power meters. The link for the EAD dataset is: https://drive.google.com/drive/folders/1zn0V6Q8eXXSKxKgcs8ZRValL5VEn3anD