Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting

Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; the optimal number of clusters for representing residential electricity use profiles is determined; and an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator’s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.

[1]  Hans-Peter Kriegel,et al.  Similarity Search on Time Series Based on Threshold Queries , 2006, EDBT.

[2]  E Bianco-Martinez,et al.  Successful network inference from time-series data using mutual information rate. , 2016, Chaos.

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

[4]  Malika Charrad,et al.  NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set , 2014 .

[5]  Bernhard Schölkopf,et al.  Kernel Mean Embedding of Distributions: A Review and Beyonds , 2016, Found. Trends Mach. Learn..

[6]  Bernard De Baets,et al.  Large-scale distance metric learning for k-nearest neighbors regression , 2016, Neurocomputing.

[7]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[8]  Yingjie Tian,et al.  A Comprehensive Survey of Clustering Algorithms , 2015, Annals of Data Science.

[9]  Krzysztof Gajowniczek,et al.  Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms , 2017 .

[10]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[11]  Alexander Mendiburu,et al.  Distance Measures for Time Series in R: The TSdist Package , 2016, R J..

[12]  Frank Klawonn,et al.  Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points , 2003, IDA.

[13]  Sinan Saraçli,et al.  Comparison of hierarchical cluster analysis methods by cophenetic correlation , 2013, Journal of Inequalities and Applications.

[14]  Fredrik Wallin,et al.  Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting , 2012 .

[15]  R. Tolimieri,et al.  Algorithms for Discrete Fourier Transform and Convolution , 1989 .

[16]  Kesheng Wu,et al.  Comparison of Clustering Techniques for Residential Energy Behavior Using Smart Meter Data , 2017, AAAI Workshops.

[17]  Wang Rui.  Synthetic Evaluation of Power Quality Based on Dynamic Time Warping Spatial Distance Measurement , 2013 .

[18]  Nora El-Gohary,et al.  Building Lighting Energy Consumption Prediction for Supporting Energy Data Analytics , 2016 .

[19]  Krzysztof Gajowniczek,et al.  Grade analysis for energy usage patterns segmentation based on smart meter data , 2015, 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF).

[20]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[21]  Alberto Gelmini,et al.  Forecasting Italian electricity market prices using a Neural Network and a Support Vector Regression , 2016, 2016 AEIT International Annual Conference (AEIT).

[22]  Uwe Aickelin,et al.  The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences Using UK Data , 2011, ArXiv.

[23]  Sanjay Bahadoorsingh,et al.  A comparison of artificial neural networks and support vector machines for short-term load forecasting using various load types , 2017, 2017 IEEE Manchester PowerTech.

[24]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[25]  Jorge Caiado,et al.  A periodogram-based metric for time series classification , 2006, Comput. Stat. Data Anal..

[26]  Jing Chang,et al.  Comparison of Random Forest and SVM for electrical short-term load forecast with different data sources , 2016, 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS).

[27]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[28]  Krzysztof Gajowniczek,et al.  Electricity forecasting on the individual household level enhanced based on activity patterns , 2017, PloS one.

[29]  Michael Lechner,et al.  The performance of estimators based on the propensity score , 2013 .

[30]  Richard A. Berk.  Classification and Regression Trees (CART) , 2008 .

[31]  Achim Zeileis,et al.  evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R , 2014 .

[32]  Brian Norton,et al.  Real-life energy use in the UK: How occupancy and dwelling characteristics affect domestic electricity use , 2008 .

[33]  Neculai Andrei,et al.  An adaptive scaled BFGS method for unconstrained optimization , 2018, Numerical Algorithms.

[34]  Yuan-Fang Wang,et al.  Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification , 2016, IEEE Transactions on Cybernetics.

[35]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[36]  Christos Faloutsos,et al.  Fast Time Sequence Indexing for Arbitrary Lp Norms , 2000, VLDB.

[37]  Yves Croissant,et al.  Panel data econometrics in R: The plm package , 2008 .

[38]  Krzysztof Gajowniczek,et al.  Grade Analysis for households segmentation based on energy usage patterns , 2017, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA).

[39]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[40]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[41]  John R. Williams,et al.  Towards Accurate Electricity Load Forecasting in Smart Grids , 2012, DBKDA 2012.

[42]  Krzysztof Gajowniczek,et al.  Short term electricity forecasting based on user behavior from individual smart meter data , 2015, J. Intell. Fuzzy Syst..

[43]  Pierpaolo D’Urso,et al.  Autocorrelation-based fuzzy clustering of time series , 2009, Fuzzy Sets Syst..

[44]  David S. Matteson,et al.  ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data , 2013, 1309.3295.