Endorsing domestic energy saving behavior using micro-moment classification

Abstract 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.

[1]  Go Hasegawa,et al.  Reducing Power Consumption in Data Center by Predicting Temperature Distribution and Air Conditioner Efficiency with Machine Learning , 2016, 2016 IEEE International Conference on Cloud Engineering (IC2E).

[2]  Zhihua Wang,et al.  Appliance Flexibility Analysis Considering User Behavior in Home Energy Management System Using Smart Plugs , 2019, IEEE Transactions on Industrial Electronics.

[3]  Peter Stokes,et al.  Micro‐moments, choice and responsibility in sustainable organizational change and transformation , 2012 .

[4]  Silvia Santini,et al.  Occupancy Detection from Electricity Consumption Data , 2013, BuildSys@SenSys.

[5]  Catherine A. Hartley,et al.  From Creatures of Habit to Goal-Directed Learners , 2016, Psychological science.

[6]  Jinlong Ouyang,et al.  Energy-saving potential by improving occupants’ behavior in urban residential sector in Hangzhou City, China , 2009 .

[7]  Kirsten Gram-Hanssen,et al.  Efficient technologies or user behaviour, which is the more important when reducing households’ energy consumption? , 2013 .

[8]  Lisa Jørgensen.  I want to show - How user-centered design methods can assist when preparing for micro moments , 2017 .

[9]  Murray Thomson,et al.  Can practice make perfect (models)? Incorporating social practice theory into quantitative energy demand models , 2014 .

[10]  Jordi Torres,et al.  Towards energy-aware scheduling in data centers using machine learning , 2010, e-Energy.

[11]  B. Gardner.  A review and analysis of the use of ‘habit’ in understanding, predicting and influencing health-related behaviour , 2014, Health psychology review.

[12]  Nittaya Kerdprasop,et al.  An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm , 2015 .

[13]  Graeme M. Burt,et al.  Self-Learning Load Characteristic Models for Smart Appliances , 2014, IEEE Transactions on Smart Grid.

[14]  Abbes Amira,et al.  The Role of Micro-Moments: A Survey of Habitual Behavior Change and Recommender Systems for Energy Saving , 2019, IEEE Systems Journal.

[15]  Xiaoli Li,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 1 Classification of Energy Consumption in Buildings with Outlier Detection , 2022 .

[16]  Philipp Geyer,et al.  Component-based machine learning for performance prediction in building design , 2018, Applied Energy.

[17]  David T. Neal,et al.  The habitual consumer. , 2009 .

[18]  Ronald A. Beghetto,et al.  Nurturing Creativity in the Micro-moments of the Classroom , 2013 .

[19]  Lars Nordström,et al.  Day-ahead electricity consumption prediction of a population of households: analyzing different machine learning techniques based on real data from RTE in France , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[20]  Saifur Rahman,et al.  Power disaggregation of combined HVAC loads using supervised machine learning algorithms , 2018, Energy and Buildings.

[21]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[22]  Damien Picard,et al.  Approximate model predictive building control via machine learning , 2018 .

[23]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[24]  R. Ying.  Motivational interviewing: helping people change , 2013 .

[25]  Zoltán Nagy,et al.  Using machine learning techniques for occupancy-prediction-based cooling control in office buildings , 2018 .

[26]  Tobias Brosch,et al.  Advances in Understanding Energy Consumption Behavior and the Governance of Its Change – Outline of an Integrated Framework , 2015, Front. Energy Res..

[27]  Abdulsalam Yassine,et al.  Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications , 2017, IEEE Access.

[28]  Vladimir Stankovic,et al.  How to make efficient use of kettles: understanding usage patterns , 2015 .

[29]  Gordon Walker,et al.  What Is Energy For? Social Practice and Energy Demand , 2014 .

[30]  Marilyn A. Brown,et al.  Machine learning approaches for estimating commercial building energy consumption , 2017 .

[31]  P. Stokes.  The Janus Dialectic of Corporate Social Irresponsibility and Corporate Social Responsibility – The Role of Micro-Moments , 2012 .

[32]  Youngdeok Hwang,et al.  Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings , 2016 .

[33]  Saul Greenberg,et al.  One size does not fit all: applying the transtheoretical model to energy feedback technology design , 2010, CHI.

[34]  Tuan Anh Nguyen,et al.  Energy intelligent buildings based on user activity: A survey , 2013 .

[35]  Miltiadis Alamaniotis,et al.  Virtual Budget: Integration of electricity load and price anticipation for load morphing in price-directed energy utilization , 2018 .

[36]  Charles Duhigg,et al.  The Power of Habit: Why We Do What We Do, and How to Change , 2012 .

[37]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[38]  E. Hobman,et al.  Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour , 2015 .

[39]  Nasrudin Abd Rahim,et al.  Long-term electric energy consumption forecasting via artificial cooperative search algorithm , 2016 .