Abstract
Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. A large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our AI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. A supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine.
Highlights
Current energy projections show that heating and cooling energy usage will skyrocket above 80% by 2030 [1]
We present an overview of the micro-moment based energy efficiency framework ( known as (EM)3) that aims to integrate behavior change theories, effective data visualization via the end-user application, and personalized recommenders to build and sustain energy-saving habits for domestic end-users
This section summarizes the current progress of the micro-moment classifier, recommender system, end-user application, and the backend
Summary
Current energy projections show that heating and cooling energy usage will skyrocket above 80% by 2030 [1]. We present an overview of the micro-moment based energy efficiency framework ( known as (EM)3) that aims to integrate behavior change theories, effective data visualization via the end-user application, and personalized recommenders to build and sustain energy-saving habits for domestic end-users. Household, the environmental parameters (e.g. indoor and outdoor temperature and humidity), and consumer’s habits profile The collection of those micro-moments defines the context of an energy-saving recommendation. THE OCCUPANCY MODULE A crucial aspect of power consumption monitoring, is to determine whether the end-user is currently occupying the room This information will aid in identifying the habits of the end-user and in turn, support more informed recommendations. C. THE TEMPERATURE AND HUMIDITY MODULE Contextual information, such as indoor temperature and humidity, aids in providing richer data on the behavior of the end-users (e.g. turning on air-conditioning in an already cool enough room). The TSL2561 light sensor, which can detect light in the range of 0.1-40,000 Lux, connected to a NodeMCU microcontroller that sends luminosity data to the backend at regular intervals
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