Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

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. Micromoments 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.

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

“…Following the micro‐moments based recommendation strategy , 34 the proposed system first detects micro‐moments of special meaning to the daily user routine. In terms of an energy efficiency recommendation system, 7,35,36 this involves the identification of user’s habitual actions, the analysis of the conditions that hold and the prediction of when actions will happen. For example, learn when the user turns the A/C on or off, in terms of time, environmental conditions, such as temperature and humidity (inside and outside).…”

A RESTful Rule Management Framework for Internet of Things Applications

“…Papaioannou et al [44] implemented a gamification approach for improving behavior-based energy savings in public buildings, which was based on a rule-based game engine. More recently, an energy efficiency framework for domestic applications has been proposed in [45]. This framework uses a supervised machine learning classifier to process data from household sensors to identify abnormalities and formulate energy-saving recommendations.…”

“…This framework uses a supervised machine learning classifier to process data from household sensors to identify abnormalities and formulate energy-saving recommendations. In comparison, while solutions in [44] and [45] have been developed for supporting energy-savings challenges, our rule-engine aims at supporting general-purpose IoT applications; furthermore, a case study evaluation has been conducted to evaluate our approach within an energy-saving case study in school buildings.…”

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

“…Besides, hundreds of dollars could be saved on end-users' energy bills annually. For such financial-saving reasons, end-users must raise their willingness to sacrifice living comforts and to draw a red line under the undue and harmful energy wasting behaviors [9]. Forgetting to unplug the charger and leaving on standby unnecessary appliances are the most common contradicted behaviors to energy management among households [10,11].…”