EMPOWERING, a Smart Big Data Framework for Sustainable Electricity Suppliers

This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received about energy bills and offering online tools. The main contributions of EMPOWERING are the creation of a novel workflow in the electricity utility sector regarding the implementation of data analytics for their customers and the fast implementation of data-mining techniques in massive datasets within a Big Data platform to achieve scalability. The results obtained show that EMPOWERING can be of use for customers of electrical suppliers by changing their energy habits to decrease consumption and so increase environmental sustainability.

[1]  Fiorella Lauro,et al.  Fault detection analysis using data mining techniques for a cluster of smart office buildings , 2015, Expert Syst. Appl..

[2]  Scott Finlinson,et al.  How Organizations Can Drive Behavior-Based Energy Efficiency , 2011 .

[3]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[4]  B. Sovacool What Are We Doing Here? Analyzing Fifteen Years of Energy Scholarship and Proposing a Social Science Research Agenda , 2014 .

[5]  Roy Fielding,et al.  Architectural Styles and the Design of Network-based Software Architectures"; Doctoral dissertation , 2000 .

[6]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[7]  Tim Jackson Motivating Sustainable Consumption , 2008 .

[8]  Kenneth A. Loparo,et al.  Big data analytics in power distribution systems , 2015, 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[9]  Cisco Visual Networking Index: Forecast and Methodology 2016-2021.(2017) http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual- networking-index-vni/complete-white-paper-c11-481360.html. High Efficiency Video Coding (HEVC) Algorithms and Architectures https://jvet.hhi.fraunhofer. , 2017 .

[10]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[11]  Kevin M. Smith,et al.  Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy , 2014 .

[12]  Fu Xiao,et al.  A framework for knowledge discovery in massive building automation data and its application in building diagnostics , 2015 .

[13]  Steve Sorrell,et al.  Reducing energy demand: A review of issues, challenges and approaches , 2015 .

[14]  Miriam A. M. Capretz,et al.  Energy Forecasting for Event Venues: Big Data and Prediction Accuracy , 2016 .

[15]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[16]  Benjamin C. M. Fung,et al.  A systematic procedure to study the influence of occupant behavior on building energy consumption , 2011 .

[17]  Paul C. Stern,et al.  Environmental Problems and Human Behavior , 1995 .

[18]  D. H. Vu,et al.  A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables , 2015 .

[19]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[20]  Peter Tzscheutschler,et al.  Short-term smart learning electrical load prediction algorithm for home energy management systems , 2015 .

[21]  W. Abrahamse,et al.  Factors influencing the acceptability of energy policies: A test of VBN theory , 2005 .

[22]  Zheng Yang,et al.  Modeling personalized occupancy profiles for representing long term patterns by using ambient context , 2014 .

[23]  Ryu Miura,et al.  Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks , 2014, IEEE Transactions on Emerging Topics in Computing.

[24]  Linda Steg,et al.  Promoting household energy conservation , 2008 .

[25]  M. M. Ardehali,et al.  LONG-TERM ELECTRICAL ENERGY CONSUMPTION FORECASTING FOR DEVELOPING AND DEVELOPED ECONOMIES BASED ON DIFFERENT OPTIMIZED MODELS AND HISTORICAL DATA TYPES , 2014 .

[26]  Shanlin Yang,et al.  Understanding household energy consumption behavior: The contribution of energy big data analytics , 2016 .

[27]  Song Guo,et al.  Big Data Meet Green Challenges: Big Data Toward Green Applications , 2016, IEEE Systems Journal.

[28]  Dominique Marchio,et al.  Development and validation of a gray box model to predict thermal behavior of occupied office buildings , 2014 .

[29]  Gianfranco Chicco,et al.  Overview and performance assessment of the clustering methods for electrical load pattern grouping , 2012 .

[30]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[31]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[32]  Alejandro Maté,et al.  Energy Consumption Prediction by Using an Integrated Multidimensional Modeling Approach and Data Mining Techniques with Big Data , 2014, ER Workshops.

[33]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[34]  W. Liu,et al.  Big Data as an e-Health Service , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[35]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[36]  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 .