Reducing energy waste in households through real-time recommendations

Publication Date

9-22-2020

Document Type

Conference Proceeding

Publication Title

RecSys '20: Fourteenth ACM Conference on Recommender Systems

Conference Location

Brazil/Virtual

DOI

10.1145/3383313.3412212

First Page

545

Last Page

550

Abstract

The energy consumption of households has steadily increased over the last couple of decades. Research suggests that user behavior is the most influential factor in the energy waste of a household. Thus, there’s a need for helping consumers change their behavior to make it more energy efficient and environment friendly. In this work we propose a real-time recommender system that assists consumers in improving their household’s energy usage. By monitoring the power demand of each appliance in the household, the system detects the device status (on/off) at any moment, and using pattern mining creates a household profile comprising energy consumption patterns for different periods of the day. An intuitive UI allows users to set energy consumption goals and preferences on the appliances they’d like to save energy from. Based on the household profile, the user’s preferences and the actual power demand the system generates personalized real-time recommendations on which appliances should be turned off at a moment. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset to model and evaluate the entire process, from data preprocessing and transformation of the appliance power demand input to various pattern mining algorithms used to generate appliance usage profiles and recommendations, showing that even small changes in appliance usage behavior can lead to energy savings between 2-17%.

Keywords

NILM, real-time recommendations, pattern mining, household energy conservation

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Department

Computer Engineering

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