SHAPE: A NEW BUSINESS ANALYTICS WEB PLATFORM FOR GETTING INSIGHTS ON ELECTRICAL LOAD PATTERNS
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1 CIRED Worksop - Rome, June 2014 SAPE: A NEW BUSINESS ANALYTICS WEB PLATFORM FOR GETTING INSIGTS ON ELECTRICAL LOAD PATTERNS Diego Labate Paolo Giubbini Gianfranco Cicco Mario Ettorre Enel Distribuzione-Italy Enel Distribuzione-Italy Politecnico di Torino-Italy Exeura-Italy diego.labate@enel.com paolo.giubbini@enel.com gianfranco.cicco@polito.it mario.ettorre@exeura.eu ABSTRACT Tis paper deals wit an innovative Web software platform for Business Analytics applied to te load patterns sourced from te Enel network s smart meters. Using ad oc developed advanced time series analysis, te SAPE (Statistical ybrid Analysis for load ProfilE) platform enables te Data Analyst to solve in a user-friendly Ric Internet Applications (RIA) important tasks suc as ourly energy analysis, customer classification, load prediction, and non-tecnical losses detection. Te platform, developed witin a R&D project, represents a tangible example of Applied Researc-Industry knowledge transfer. By means of a Web Service interface, te platform exposes and sares te implemented models wit oter Corporate or tirdparty applications. Te SAPE datawareouse currently stores tree years progressively updated of load patterns for about 100,000 measurement points at te customers premises. INTRODUCTION In today s global world, generating new knowledge and turning it into innovative products and services is crucial to maintain and enance industry competitiveness. Transforming te results of scientific researc into new products is, owever, a complex process involving a broad range of actors. Profitable results may come from University-Industry R&D interactions. Te transition from supplier-centric to customer-centric framework carried out by Enel after te liberalization of te electricity market needs not only an efficient and effective smart metering system, but also requires new business strategies and innovative services. After 13 years from te first smart meter installed, Enel is today te utility wit te largest Automated Meter Reading/Management (AMR/AMM) infrastructure in te world consisting of more tan 31 millions of smart meters remotely managed in Italy. From te first Enel smart meter, te energy market as been subject to significant canges, including EU corporate unbundling of te energy sector. Te electricity market actors ave now to face new callenges, suc as better customer services provision and tariff definition, energy quality supply enancement, tecnical and non-tecnical losses reduction, medium- and low-voltage network update to deal wit renewable energy producers (two-way active energy flows), as well as rationalization of te network investment costs. Eac market participants needs new business strategies in order to meet stringent requirements. For tis purpose, te problem of caracterizing te load and predicting te consumption beavior as been recognized as relevant, and te tecnological improvement in te metering devices as leveraged various issues in load pattern data management. Wit te introduction in te recent years of AMR/AMM systems in many countries, tere as been a growing interest in developing applications based on load pattern data, mainly due to te ability of te underlying electronic meters tecnology to record data at a relative low cost consumption at 1-60 minutes resolution, rater tan montly. Te knowledge on te sape of te electricity consumption as gained increasingly iger importance, wit te aim of partitioning te consumers into a number of classes representing te actual usage of electricity during time, assisting enanced load prediction or non-tecnical losses detection [1]-[4]. Te current approaces refer to managing te data of a large amount (i.e., many tousands) of customers [5][6]. Tanks to te AMR/AMM system called Telegestore, Enel is today potentially able to measure and collect remotely a large amounts of consumption patterns recorded on a 15 minute basis from more tan 31 millions of customers. Te extraction of idden knowledge from tis uge amount of data requires stateof te-art tecnologies based on Data Mining and Macine learning and approaces on Big Data [7]. In tis context, Enel Distribuzione as recently started te R&D project SAPE in collaboration wit Politecnico di Torino as te scientific researc partner and Exeura as te industrial software-development partner. Te SAPE project represents a tangible example of Researc-Industry knowledge transfer. Taking te results of te Applied Researc pase, te final objective of te project is te development of a Business Analytics Web platform as a flexible and extensible Data Mining suite tailored for te electrical domain, allowing Analysts build teir own analytics workflow in a user-friendly Ric Internet Application (RIA). Te platform consists of te modules : 1. Datawareouse management 2. Basic load analysis 3. Customer load classification 4. Load prediction 5. Non-tecnical losses detection support Paper No 0354 Page 1 / 5
2 CIRED Worksop - Rome, June 2014 Eac module, in turn, is divided into sub-modules. At te moment of te paper submission, te development of modules 4 and 5 is ongoing and will be described in a next work. Currently te SAPE datawareouse (DW) stores information from about 100,000 Low Voltage (LV) smart meters, gatered starting from January 2011 and currently in progress, including about 1,000 balance measurement points installed witin MV/LV substations. All information are treated for statistical purposes, in aggregate and anonymous form. DATAWAREOUSE & BASIC LOAD ANALYSIS Te SAPE platform is provided wit its own DW (Figure 1). All te necessary data suc as daily load patterns, tecnical and commercial related information, temperature data, are provided from different Company systems data source. In te SAPE context, a daily load pattern is a 96-point time series in wic eac point represents te energy in te last 15-minute interval. Te patterns refer to active power consumption, active power production, and reactive power. Terefore, eac year, for eac customer s measurement point, SAPE stores 3*96*365=105,120 measured data. Considering tat te current number of measurement points already stored in te DW is expected to grow, retrieval and access tis big quantity of data required efficient storage solutions also to enable a real-time experience to te user. Table 1 represents te amount of required storage space and number of points to be stored for a year-long load data. Time Interval #Smart Meters Daily Load data Amount of Data Amount of Measures 1 Day 100K 1 KB 98MB 29Mln 1 Mont 100K 1 KB 3GB 864Mln 1 Year 100K 1 KB 36GB 10.4Bln Table 1 Amount of Load data Figure 1 SAPE Arcitecture Load pattern storage and retrieval For load pattern storage, te File System implementation cosen is based on te open source Apace indexing tecnology Lucene TM. Lucene is a full-text searc library in Java wic adds searc functionality to an application or website by adding content to a full-text index. Lucene is able to acieve fast searc responses because, instead of searcing te text directly, it searces an index in a very efficient manner. In te SAPE specific application a Lucene Document, corresponds to a daily load pattern. A Document is a sequence of Fields. A Field is a <namevalue> pair. In te SAPE application, one field represents a daily load pattern stored as binary blob, oter fields represent date, identifier, etc. Field values may be stored, indexed and/or analyzed. Tis kind of implementation as reduced retrieval and update time wen te overlying functionality is requiring it. Oter data suc as tecnical and commercial information, ave been stored in a Relational DBMS. Figure 2 sows te time comparison for querying data between 2 different Data Layer implementations. In a first solution named Database, 30 millions of daily load patterns ave been stored using Microsoft SQL server 2008R2 (Intel Xeon CPU E7@2.00Gz 4 processor, 24 GBRAM, RAID 5 storage) wit tables structured so tat eac 96- point load pattern is stored as a binary blob. In te second solution, te same load patterns ave been indexed and stored in te server s file system by means of te structure provided by Lucene. Ten, we measured te retrieval time of a growing set of load patterns, repeating te same specific query 5 times and ten calculating te average time. We executed te same queries using te Lucene's own interrogation language. Te results sown in Figure 2 demonstrate tat te storage solution based on Lucene is more time-efficient in retrieving te information tan te RDBMS solution. Data cleaning operations on load patterns are carried out during periodic DW update, wereas missing values are andled by means of te Load prediction module. No Paper No 0354 Page 2 / 5
3 CIRED Worksop - Rome, June 2014 data dimensionality reduction is performed at tis stage. At te end of eac load patterns update (typically montly), te application reports te details about te data quality stored in te DW and te errors detected and automatically corrected. By means of local storage support, SAPE also enables te user to upload spot load patterns, to analyze tem in a private area, witout compromise te main DW. LOAD PROFILES GEOGRAPICAL DISTRIBUTION AND IDC a) spatial coverage Figure 2 Lucene vs. RDBMS time comparison Load patterns geograpical coverage One of te most important basic functionalities planned during te design of te platform as been to ensure te operator to obtain knowledge about geograpical load pattern coverage, jointly wit some related indexes. By proper interface, te user is able to visualize colored territorial nodes, as a function of amount of daily load patterns stored in te DW among te teoretical value in a given time period, defined as te product measurement points*days (IdCA index, Figure 3.a). In a different matrix component interface, te user can visualize details about daily patterns coverage (Figure 3.b). Suitable ad oc coverage indexes ave been developed. Oter modules Furter basic analysis refers to tese sub-modules: Load geograpical aggregator Load pattern viewer (Figure 4) Time-of-use energy geograpical analysis MV/LV substation energy balance assessment CUSTOMER CLASSIFICATION Altoug classification and clustering are often mentioned in te same breat, tey are based on different analytical approaces. In SAPE, customer segmentation indicates te process of classes discovery wic is generally carried out troug clustering, and customer classification is te process in wic, given a previous customers classes partition, a classification model is induced troug a supervised training pase based on classes labels. New customers, for wic te outcome class is unknown, are presented to te model and scored accordingly. b) daily coverage Figure 3 - Load pattern coverage near Milan in a given time period (a) and details per day (b) Figure 4 Load pattern viewer Customer segmentation is te process of dividing customers into distinct, meaningful and omogeneous subgroups based on various attributes and caracteristics. Te type of segmentation used depends on te specific business objective. For te SAPE s objectives one of te first task implemented was to reveal, for te first time in Italy, te customers consumption classes at te National level based on consumption beavior. Te Enel s Italian LV customer base was sampled taking into account te caracteristic variable annual energy. Troug a pre-sampling test we ad establised tat te annual energy was quite eterogeneous. Terefore, in order to reduce te cost of te survey (load patterns extraction) and at te same Paper No 0354 Page 3 / 5
4 CIRED Worksop - Rome, June 2014 time increase te accuracy, a stratified sampling tecnique [8] as been developed and implemented, rater tan simple sampling. Te consumption points (customers) ave been grouped in = 10 macrocategories (strata) according to teir commercial categories, as follows: 1. Residential ouseolds 2. Transport 3. Agriculture 4. Industry 5. Commercial 6. Public Ligting 7. General Building Services 8. eat Pumps 9. Non-Residential ouseolds 10. Generation (Producer/Prosumer) To represent te variability inside a single stratum, te annual energy consumption as been used as te representative feature. Starting from te number of customers N belonging to eac stratum = 1,, and from te mean value and standard deviation of te representative feature (obtained from te Company s databases), te statistically significant total number of points n as been determined for a given confidence probability (99%) wit associated multiplier k of te estimated standard deviation and per cent amplitude d% of te confidence interval referring to te mean value of te representative feature, as follows: n 1 d %, k 2 1 N 2 2 N d% N 1 100k N N N For example, wit 99% confidence probability (k = 2.58) and d% = 5% te total number n d%, k is about 17,000. Correspondingly, te statistically significant number of point n for eac stratum = 1,, as been obtained. In addition, eac macro-category can be partitioned into sub-categories on te basis of te reference power. For eac macro-category, different clustering procedures ave been executed using te n measurement points on te complete load patterns for one year, wit te aim to reveal te best inner partition of eac macro-category into classes and obtain te class-related typical load profiles. Rater tan consider seasons as separate periods, load seasonality as been assessed by means of a data-driven dedicated algoritm. Te results indicate 21 main customers classes and 82 typical active energy load profiles associated to distinct periods of te year. A decision tree classification algoritm as been trained from te 82 typical load patterns. A classification model as assessed, calculating for eac macro-category te related classification performance. Customer Segmentation and Classification is a task repeatable by means of te SAPE Workflow interfaces. Customers can be segmented by coosing active consumption, active production, or reactive energy. Assisted by a suitable geograpical map, te Analyst starts creating a new experiment and selecting any measurement points aggregation. Te Analyst can ten create a Customer Segmentation workflow supported and boosted by a variety of ready-to-run plugins and algoritms tailored for energy analytics, some of wic are described in Table 2. At te end of te Workflow configuration, SAPE executes te selected algoritms and visualizes te results, as well as adequate measures, in order to assess te revealed model and classes (Figure 5). For eac step in Table 2, multiple coices can be made. In tis case SAPE generates a number of runs, obtained by te Cartesian product of te different coices and comparing ten te different results. Workflow Step 1.Customers & time period selection 2.Preprocessing options 3.Clustering algoritms 4.Partition adequacy measures I level parameterization Aggregation or Macro-category II level parameterization Optional statistical significance test Normalization Contract power, Mean, Max, Min/(Max-Min) Scale reduction 1 24 ours, 1 week, etc. load pattern mean Weekday, Saturday, oliday, Season, Mont, etc. Low consumption Filter out te load load pattern filter patterns wit more ten a given percentage of data below a given tresold ierarcical (wit Specific parameters different linkage for te selected criteria), Follow algoritm te Leader, K- means, Support Vector Clustering, ybrid metods. CDI - Clustering Dispersion Indicator, DBI - Davies-Bouldin Index, MDI - Modified Dunn Index, MIA - Mean Index Adequacy, SI - Scatter Index, SMI - Similarity Matrix Indicator, WCBCR - Ratio of Witin Cluster sum of squares to Between Cluster variation 5.Classification Model selection and parameterization Table 2 Customer Segmentation and Classification Workflow parameters Paper No 0354 Page 4 / 5
5 CIRED Worksop - Rome, June 2014 Figure 5 - Segmentation results viewer PARALLEL COMPUTING TECNIQUES Te Analytics layer in SAPE exploits many-core programming tecniques tat are particularly effective in time series mining area, tanks to inerently ig data dimensionality (number of time series on wic computation takes place, as well as number of samples per eac time series). Most of te implemented algoritms ave been carefully crafted using OpenCL TM tecnology in order to distribute work among nvidia GPU s computation unit wile keeping work-item queues always full during bot data pre-processing and mining pases. Tis required also automatic tuning of split level (number of many-core treads working on a local group in order to produce a result) and vectorization strategy, cosen by SAPE in order to maximize performance and local memory allocation of underlying OpenCL-compliant device. In tasks were many-core programming is not advisable due to insufficient data dimensionality or independence among data cunks, SAPE uses work decomposition matced wit a fork-join pool to keep all server's CPUs busy during computations. CONCLUSION AND FUTURE WORK Transforming te results of scientific researc into new products is a complex process involving a broad range of actors. Te Business Analytics platform implemented in te SAPE project is te result of a successful Researc-Industry knowledge transfer. Te results delivered by te SAPE project are of strategic importance, as tey will provide decision support to current business processes and identification of new ones. Te knowledge of te typical patterns of te aggregated energy consumption/production brings new understanding, directly available in SAPE, on ow te loads sould be estimated in network applications. Te economic benefit of understanding and predict customer loads will decrease investment costs by better matcing long-term planning needs, will assist tecnical losses evaluation and allow better estimation of te economic losses resulting from service interruptions. Te Nontecnical losses module will support te verification of energy frauds and metering anomalies. Te basic analyses are also suitable for directly using te real load patterns, enabling te calculation of time-dependent energy balances. Te summary information on te consumption will be useful for more effective management of te electricity distribution network in synergy wit existing initiatives in te Smart Grids field. Te SAPE platform is ongoing at te moment of paper submission. Te next modules to be released concern Load prediction and Non-tecnical losses detection, in wic new findings in applied scientific researc (to be reported in te future) are being implemented. Future refinements may include extensive analysis of te impact of prosumers contributions on te networkrelated variables (voltage, current, losses, reliability). On te side of Industrial software development, te enancements deal wit design and implementation of an arcitecture ready to manage Big data for a long time period wit quick response time and ig level of reliability and scalability, distributing bot data and computations by means of appropriate tecnology. ACKNOWLEDGMENTS Te autors wis to tank Lorenzo Gallucci, Antonio Notaristefano and Federico Piglione for teir insigts and implementation of te procedures. REFERENCES [1] G.Cicco, R.Napoli, P.Postolace, M.Scutariu and C.Toader, Customer Caracterization Options for Improving te Tariff Offer, IEEE Trans. on Power Systems, vol.18, no.1, February 2003, pp [2] G.Cicco, R.Napoli, F.Piglione, M.Scutariu, P.Postolace and C.Toader, Emergent Electricity Customer Classification, IEE Proc. Gener. Transm. and Distrib., vol.152, no.2, Marc 2005, pp [3] L.M. Saini, and M.K. Soni, Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi- Newton metods, IEE Proc. Gener., Transm. and Distrib., vol.149, no.5, Sept. 2002, pp [4] S.S.S.R. Depuru, L. Wang, and V. Devabaktuni, Electricity teft: Overview, issues, prevention and a smart meter based approac to control teft, Energy Policy, vol. 39, no. 2, February 2011, pp [5] M. Koivisto, P. eine, I. Mellin, and M. Letonen, Clustering of Connection Points and Load Modeling in Distribution Systems, IEEE Trans. on Power Systems, vol. 28, no. 2, February 2013, pp [6] S. Ramos, J.M. Duarte, F.J. Duarte, Z. Vale, P. Faria, A data mining framework for electric load profiling, Proc. IEEE PES Conference on Innovative Smart Grid Tecnologies Latin America (ISGT LA), São Paulo, SP, Brazil, April [7] Y. Simman, S. Aman, A. Kumbare, R. Liu, S. Stevens, Q, Zou, and V. Prasanna, Cloud-Based Software Platform for Big Data Analytics in Smart Grids, Computing in Science & Engineering, vol. 15, no. 4, July/August 2013, pp [8] J. Neyman, On te two different aspects of te representative metod: te metod of stratified sampling and te metod of purposive selection, Journal of te Royal Statistical Society, Part IV, 1934, pp Paper No 0354 Page 5 / 5
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