Customer behaviour and data analytics

The present evolution of the electrical sector is paying increasing attention to the customer needs. This paper deals with a number of aspects addressed in the present discussions on the characteristics of individual and aggregate consumers, referring to the shape and flexibility of the demand. Specific points addressed include the role of metering, how to obtain knowledge from load pattern disaggregation and clustering, customer-related effects of microgrids development, and active users' participation in demand response initiatives.

[1]  Christoph M. Flath,et al.  Quantifying load flexibility of electric vehicles for renewable energy integration , 2015 .

[2]  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.

[3]  Luis F. Ochoa,et al.  Evaluating and planning flexibility in sustainable power systems , 2013, 2013 IEEE Power & Energy Society General Meeting.

[4]  Michael Conlon,et al.  A clustering approach to domestic electricity load profile characterisation using smart metering data , 2015 .

[5]  Mohamed Chaouch,et al.  Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-Day Household-Level Load Curves , 2014, IEEE Transactions on Smart Grid.

[6]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[7]  P. Charpentier,et al.  Statistical Estimation of the Residential Baseline , 2016, IEEE Transactions on Power Systems.

[8]  Pierluigi Mancarella Cogeneration systems with electric heat pumps: Energy-shifting properties and equivalent plant modelling , 2009 .

[9]  Haitham Abu-Rub,et al.  Smart grid customers' acceptance and engagement: An overview , 2016 .

[10]  D. S. Kirschen,et al.  Flexibility from the demand side , 2012, 2012 IEEE Power and Energy Society General Meeting.

[11]  Gianluca Sapienza,et al.  Robust Real-Time Load Profile Encoding and Classification Framework for Efficient Power Systems Operation , 2015, IEEE Transactions on Power Systems.

[12]  Francesco Piazza,et al.  Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview , 2015, 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC).

[13]  Abhay Gupta,et al.  Is disaggregation the holy grail of energy efficiency? The case of electricity , 2013 .

[14]  Pierluigi Mancarella,et al.  Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[15]  Yi Wang,et al.  Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications , 2016, IEEE Transactions on Smart Grid.

[16]  Sangtae Ha,et al.  Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility , 2012, IEEE Journal on Selected Areas in Communications.

[17]  Johann Kranz,et al.  The role of smart metering and decentralized electricity storage for smart grids: The importance of positive externalities , 2012 .

[18]  Karl Aberer,et al.  When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers , 2014, IEEE Transactions on Smart Grid.

[19]  Jie Lu,et al.  A New Index and Classification Approach for Load Pattern Analysis of Large Electricity Customers , 2012, IEEE Transactions on Power Systems.

[20]  Vivek Ashok Bohara,et al.  Appliance Activity Recognition Using Radio Frequency Interference Emissions , 2016, IEEE Sensors Journal.

[21]  David C. H. Wallom,et al.  Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles , 2015, IEEE Transactions on Power Systems.

[22]  Michael E. Webber,et al.  Clustering analysis of residential electricity demand profiles , 2014 .

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

[24]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[25]  Shiyin Zhong,et al.  Hierarchical Classification of Load Profiles Based on Their Characteristic Attributes in Frequency Domain , 2015, IEEE Transactions on Power Systems.

[26]  Gianfranco Chicco,et al.  Electrical Load Pattern Grouping Based on Centroid Model With Ant Colony Clustering , 2013, IEEE Transactions on Power Systems.

[27]  E. Lannoye,et al.  Evaluation of Power System Flexibility , 2012, IEEE Transactions on Power Systems.

[28]  Mikhail Simonov Event-Driven Communication in Smart Grid , 2013, IEEE Communications Letters.

[29]  Ram Rajagopal,et al.  Household Energy Consumption Segmentation Using Hourly Data , 2014, IEEE Transactions on Smart Grid.

[30]  Gianfranco Chicco,et al.  Formation of load pattern clusters exploiting ant colony clustering principles , 2013, Eurocon 2013.

[31]  W. Fichtner,et al.  Electricity load profiles in Europe: The importance of household segmentation , 2014 .

[32]  Peter Lund,et al.  Review of energy system flexibility measures to enable high levels of variable renewable electricity , 2015 .

[33]  J. Zico Kolter,et al.  Contextually Supervised Source Separation with Application to Energy Disaggregation , 2013, AAAI.

[34]  J. Widén,et al.  Forecasting household consumer electricity load profiles with a combined physical and behavioral approach , 2014 .

[35]  Helge V. Larsen,et al.  Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers , 2013 .

[36]  Gianfranco Chicco,et al.  Underlying concepts for event-driven energy metering , 2015, 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP).

[37]  Jinye Zhao,et al.  A Unified Framework for Defining and Measuring Flexibility in Power System , 2016, IEEE Transactions on Power Systems.

[38]  Keun Ho Ryu,et al.  Subspace Projection Method Based Clustering Analysis in Load Profiling , 2014, IEEE Transactions on Power Systems.

[39]  Gianfranco Chicco,et al.  Emergent electricity customer classification , 2005 .

[40]  Jiguo Cao,et al.  Automated Load Curve Data Cleansing in Power Systems , 2010, IEEE Transactions on Smart Grid.

[41]  Yasuhiro Hayashi,et al.  A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households , 2013, IEEE Transactions on Smart Grid.

[42]  José Luis Díez,et al.  Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers , 2014 .

[43]  Thomas G. Habetler,et al.  Electric Load Classification by Binary Voltage–Current Trajectory Mapping , 2016, IEEE Transactions on Smart Grid.

[44]  Gianluca Zanetto,et al.  Knowledge extraction from event-driven metering of electrical consumption patterns , 2016, 2016 Power Systems Computation Conference (PSCC).

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

[46]  Mario Berges,et al.  Unsupervised disaggregation of appliances using aggregated consumption data , 2011 .

[47]  Rajab Khalilpour,et al.  Leaving the grid: An ambition or a real choice? , 2015 .

[48]  Enrico Carpaneto,et al.  Probabilistic characterisation of the aggregated residential load patterns , 2008 .

[49]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.

[50]  P. Postolache,et al.  Customer Characterization Options for Improving the Tariff Offer , 2002, IEEE Power Engineering Review.

[51]  Tobias Boßmann,et al.  Model-based assessment of demand-response measures—A comprehensive literature review , 2016 .

[52]  Le Xie,et al.  A Metric and Market Construct of Inter-Temporal Flexibility in Time-Coupled Economic Dispatch , 2016, IEEE Transactions on Power Systems.

[53]  Ram Rajagopal,et al.  Smart Meter Driven Segmentation: What Your Consumption Says About You , 2013, IEEE Transactions on Power Systems.

[54]  Mikhail Simonov Hybrid Scheme of Electricity Metering in Smart Grid , 2014, IEEE Systems Journal.

[55]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[56]  Guilin Zheng,et al.  Residential Appliances Identification and Monitoring by a Nonintrusive Method , 2012, IEEE Transactions on Smart Grid.

[57]  Vahid Vahidinasab,et al.  Customer baseline load models for residential sector in a smart-grid environment , 2016 .

[58]  Ming Dong,et al.  Non-Intrusive Signature Extraction for Major Residential Loads , 2013, IEEE Transactions on Smart Grid.

[59]  Gerard Ledwich,et al.  Demand Response for Residential Appliances via Customer Reward Scheme , 2014, IEEE Transactions on Smart Grid.

[60]  Giuseppe Marco Tina,et al.  Load Demand Disaggregation Based on Simple Load Signature and User's Feedback , 2015 .

[61]  Muhammad Ali Imran,et al.  Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey , 2012, Sensors.

[62]  Pierluigi Mancarella,et al.  Flexible distributed multienergy generation system expansion planning under uncertainty , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[63]  Yu-Hsiu Lin,et al.  Non-Intrusive Load Monitoring by Novel Neuro-Fuzzy Classification Considering Uncertainties , 2014, IEEE Transactions on Smart Grid.

[64]  A. Notaristefano,et al.  Data size reduction with symbolic aggregate approximation for electrical load pattern grouping , 2013 .

[65]  Chris Develder,et al.  Two-Stage Load Pattern Clustering Using Fast Wavelet Transformation , 2016, IEEE Transactions on Smart Grid.

[66]  Pierluigi Mancarella,et al.  Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users' discomfort , 2015, 2016 IEEE Power and Energy Society General Meeting (PESGM).