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Domestic user behavior is a crucial factor guiding overall power consumption, necessitating the development of systems that analyze and help shape energy-efficient behavior. Therefore, the most important step in the process is the collection and understanding of highly detailed domestic consumption data. This article presents an appliance-based energy data collection and analysis system for energy efficiency applications. It leverages the concept of micro-moments, which are short-timed and energy-based events that form the overall energy behavior of the end user. The system comprises sensing modules for recording energy consumption, occupancy, temperature, humidity, and luminosity storing recordings on a database server. Sensing parameters were tested in terms of connection stability and measurement accuracy. A four-week contextual appliance-level dataset has been collected from research cubicles. Collected data were also classified into corresponding micro-moments with a variety of classifiers including ensemble decision trees and deep learning, achieving high stability and accuracy of 99%. Further, the micro-moment usage efficiency is calculated to quantify the efficiency of usage at the appliance level. 2021 IEEE. Manuscript received November 4, 2019; revised March 23, 2020; accepted April 22, 2020. Date of publication June 9, 2020; date of current version March 9, 2021. This article was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. (Corresponding author: Abdullah Alsalemi.) Abdullah Alsalemi, Yassine Himeur, and Faycal Bensaali are with the Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar (e-mail: a.alsalemi@qu.edu.qa; yassine.himeur@qu.edu.qa; f.bensaali@qu.edu.qa). Scopus

Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming. A variety of broad approaches have been developed by both industry and the research community. However, there is an ever increasing need for comprehensive, end-to-end solutions aimed at transforming human behavior rather than device metrics and benchmarks. In this paper, a micro-moment-based smart plug system is proposed as part of a larger multi-appliance energy efficiency program. The smart plug, which includes two sub-units: the power consumption unit and environmental monitoring unit collect energy consumption of appliances along with contextual information, such as temperature, humidity, luminosity and room occupancy respectively. The plug also allows home automation capability. With the accompanying mobile application, end-users can visualize energy consumption data along with ambient environmental information. Current implementation results show that the proposed system delivers cost-effective deployment while maintaining adequate computation and wireless performance., Comment: This paper has been accepted in SCA2020: The Fifth international conference on Smart City Applications

When designing a large scale IoT ecosystem, it is important to provide economical solutions at all levels, from sensors and actuators to the software used for analytics and orchestration. It is of equal importance to provide non-intrusive solutions that do not violate users' privacy, but above all, it is important to guarantee the accuracy and integrity of the provided solution. In this work, we present a research prototype solution that has been developed as part of an ongoing project called (EM)3. The project involves IoT sensors and actuators, realtime data analytics modules and cutting edge recommendation algorithms in an ecosystem that improves energy efficiency in office buildings. The main concept of the (EM)3 is to recommend energy saving actions at the right moment to the right user. At the core of the (EM)3 vision is to detect when is the right moment for an energy saving action and sensors play a vital role in this. This article focuses on the model that predicts room occupancy using only data from a motion sensor. The predictions of the model, are used to trigger automations and notifications that turn-off office devices (e.g. air conditioning, lights, monitors, etc.) as soon as the office becomes empty, or a few minutes before this happens, in order to further promote efficient energy consumption habits. The evaluation of the model, using data from a camera sensor for validation, demonstrates a very low error rate and a very short delay on the detection of when the room is actually empty. 2020 IEEE. ACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Domestic user behavior shapes overall power consumption, necessitating the development of systems that analyze and help foster energy efficient behavior. The most important step in the process is the collection and management of comprehensive data on end-user power consumption behavior. This paper presents an appliance-based energy data collection system for domestic households. It revolves around the concept of micro-moments, which are short-timed and energy-based events that form the overall energy behavior of the end-user. The system comprises sensing modules for recording energy consumption, occupancy, temperature, humidity, and luminosity storing recordings on a database server. Sensing parameters were tested in terms of connection stability and measurement accuracy. High stability and accuracy benchmarks have been reached with future work focused on deploying the system for multi-users in addition to enhancing overall data security and integrity. 2019 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Among the large number of efforts that study the role of technology in energy saving, there exists, first, frameworks for monitoring and controlling energy consumption in households, second, systems that suggest best practices to users and energy providers, and third, findings that focus on the motivations that trigger a behavioral change toward energy efficiency. However, there is still no work that builds on the habitual behavior change of individuals and the use of technology for this purpose. In this paper, we survey the literature that aims at understanding the behavior of energy consumers and then changing it by recommending energy-saving activities. We build on the concept of 'micro-moments' and the 'habit loop' theory to explain how we can gradually change user routines by targeting those specific moments where the proper recommendation and a small but instant reward can be more effective in shaping a better energy-efficient profile. - 2007-2012 IEEE. Manuscript received October 25, 2018; revised January 28, 2019; accepted February 12, 2019. Date of publication March 25, 2019; date of current version August 23, 2019. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. (Corresponding author: Abdullah Alsalemi.) A. Alsalemi and F. Bensaali are with the Department of Electrical Engineering, Qatar University, Doha 2713, Qatar (e-mail:, aa1300250@qu.edu.qa; f.bensaali@qu.edu.qa). Scopus

With the ever-growing rise of energy consumption and its devastating financial and environmental repercussions, it is of utmost significance to moderate energy usage with proper energy efficiency tools. This is particularly applicable to domestic energy end-users, where an accurate profile is a prerequisite for motivating energy saving behavior. This article presents an innovative method for accurately understanding domestic energy usage patterns through a classification system. It capitalizes on the emerging concept of micro-moments, short energy-related events, and builds a comprehensive profile of end-user's energy activities with unprecedented accuracy. Micro-moments are classified based on a set of criteria per the given appliance. Five classifiers with different parameter settings were trained and tested on 10-fold cross-validated simulated data, with ensemble bagged trees topping with an accuracy of 88.0%. We also observed that linear classifiers lack in accuracy due to their inability to capture the dataset's specific structure and patterns. Fused with the other components of our framework, the proposed classification system is a novel contribution to domestic energy profiling in an effort to step energy efficiency up to the next level. - 2019 Elsevier Ltd This paper was made possible by National Priorities Research Program (NPRP) Grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

To reduce energy usage and CO2 emission due to heating, heat pumps have turned out a good option. For example, to obtain a net zero house, often a combination of solar panels and a heat pump is used. A computational model of the performance of a heat pump provides a useful tool for prediction and decision making. In this paper, six variations of such computational models are discussed and evaluated. Evaluation was based on real world empirical data for 8 different domestic situations. The evaluation took place by determining the most optimal values for the parameters of each of the models for the given data, and then considering the remaining error.