The Role of Micro-Moments: A Survey of Habitual Behavior Change and Recommender Systems for Energy Saving

Alsalemi, Abdullah; Sardianos, Christos; Bensaali, Fayçal; Varlamis, Iraklis; Amira, Abbes; Dimitrakopoulos, George

doi:10.1109/jsyst.2019.2899832

Cited by 65 publications

(42 citation statements)

References 63 publications

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“…The (EM) 3 ecosystem is designed to be built upon the cutting edge of energy efficiency recommendation systems for higher domestic energy efficiency that relies on both evidence and technology [17], [21]. The framework includes sub-systems that work together building on the habit loop.…”

Section: Data Collectionmentioning

confidence: 99%

“…However, more definitions have been provided. According to [17], for instance, the ''I want to change'' micro-moment can be employed as an enabler to raise awareness about energy efficiency and as the kindle for behavior change. Comprehensive understanding of domestic energy consumption can be achieved by the collection and analysis of energy micro-moments, which are short, time-based events at which an end-user performs an energy behavior.…”

Section: Introductionmentioning

confidence: 99%

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Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

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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.

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Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

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“…Google categorized micro-moments as follows: <I-want-to-know>, <I-want-to-go>, <I-want-to-do>, and <I-wantto-buy>. Inspired by this idea, recent works exploring the use of micro-moments to monitor, analyze, and understand energy consumption behavior have been presented [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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

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Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.

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“…In this case, it is important either to automate energy efficiency, by embedding intelligence to the devices (actuators) and the environment (sensors), or to gradually change people’s habits and promoting more energy‐efficient behaviors, through warnings, notifications, and up‐to‐time recommendations 4–6 . Despite the many works that used feedback for persuading home users to improve their energy consumption behavior, it was in Reference [7] that action recommendations have been addressed in real‐time to home users, in association to their actions and daily habits.…”

Section: Introductionmentioning

confidence: 99%

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

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The Role of Micro-Moments: A Survey of Habitual Behavior Change and Recommender Systems for Energy Saving

Cited by 65 publications

References 63 publications

Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

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

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

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