Predictive Analytics And Machine Learning For Utilities

Predictive Analytics and Machine Learning for Utilities Андрій Тищенко SAP PUBLIC Ukraine

Disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. Except for your obligation to protect confidential information, this presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or any related document, or to develop or release any functionality mentioned therein. This presentation, or any related document and SAP's strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this presentation is not a commitment, promise or legal obligation to deliver any material, code or functionality. This presentation is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This presentation is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this presentation, except if such damages were caused by SAP’s intentional or gross negligence. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 2

Predictive Analytics and Machine Learning Analytics / BI Aggregate Data Visualize Humans make decision Data is aggregated for visualization UI integration at best Train Model Prepare Data Apply Model Machines propose/make decision Data is de-normalized, flattened, fine grain Process integration at last Monitor Advanced Analytics / Predictive Analytics 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 3

Data Science & Machine Learning Portfolio Data Scientists & Citizen Data Scientists Line of Business User SAP Analytics Cloud Data Manager Automated Modeler Expert Modeler (Visual Composition Framework) Predictive Factory SAP Predictive Analytics Spatial DB2 Oracle Teradat a etc Streaming Analytics SAP Applications Graph Series Data Predictive (PAL/APL) HANA Predictive & Machine Learning Hadoop / Spark Vora DB Text Analytics On Premise SAP HANA Hadoop 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC SAP Fraud Management Developers and Data Scientists Functional services Business services HANA DBaaS Leonardo Machine Learning Foundation Big Data Services SAP Cloud Platform 4

SAP Predictive Analytics Core workflows Prepare Data with Data Manager Build analytical datasets with clicks, not code Create thousands of derived features to increase predictive accuracy Build robust Predictive Models quickly with Automated Modeler Automate dataset production & create reusable transformations Identify which variables are changing over time with timestamped populations Generate automatically reusable SQL code with associated documentation 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC Build complex Predictive Pipelines with Expert Analytics Easy to Use - Drag-and-drop data selection, preparation and predictive modelling Use the predictive models in SAP HANA such as Unified Demand Forecast (UDF), Predictive Analytics Library (PAL) & APL Automate executive and operational reports In Database Execution – Automated Predictive Library (APL) on SAP HANA & Native Spark Modelling on Hadoop Leverage 8000 existing R functions and libraries Embed the models in external SAP applications Automate Predictive Modelling with Classification, Regression, Clustering, Time Series Forecast, Association Rules Identify automatically of key contributing variables on very wide datasets 5

SAP Predictive Analytics Core workflows Link Analysis & Recommendation Extract variables for enhanced link analysis and prediction Identify communities amongst your customers Find influencers within communities to focus efforts where they count the most Create personalized recommendations for each visitor 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC Scoring Operationalization with Predictive Factory In-database scoring using SQL Interface with business applications using scoring equations and code: SQL, Java, PMML, SAS, C, C Manage lifecycle of thousands of models in parallel, whatever their origin (Automated Modeler & Expert Analytics) Real Time Scoring on SAP HANA and Spark Streaming environments Schedule model automated application to new data Detect data deviation & retrain model automatically when required Event and time based scheduling Segmented Time Series Modelling 6

Two complementary approaches to Machine Learning Answering Experts and Business Analysts Complementary Modeling Approachs Automated Modeler Guided Workflow for all users – No coding (low touch user experience) Maximize user productivity through totally automated model selection Support all major Data Mining functions (Time Series, Classification, Regression, Clustering, Link Analysis and Recommendation) In-database apply on all platforms In-database training on SAP HANA & Hadoop All data sources for training & apply (ODBC connections) 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC Expert Analytics Predictive Pipelines for advanced users (high touch user experience) Build Predictive Pipelines using a drag and drop editor with support for native predictive libraries including PAL, APL & R on SAP HANA Advanced configuration of Automated Modeler Nodes (APL) Ready for Massive Modelling including support for Data Manager (Automated Data Preparation) & Predictive Factory (Industrialization) Self-sufficient end to end user experience for Data Scientists with support for Datasets prepared in Data Manager 7

Machine Learning in Applications Accurate predictions and advanced analytics insights for business decision makers SAP Predictive Analytics Predictive Analytics Integrator (PAI) SAP S/4HANA Publish Models SAP Analytics Cloud C4C Prepare & refine models Machine Learning platform for Data Scientists and Business Analysts to create, manage and monitor predictive models to answer to business issues 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC Framework for SAP Developers to embed Machine Learning algorithms in SAP Applications 8

SAP Predictive Analytics Integrator (PAI) Machine Learning Framework for SAP Applications PAI enables applications to deliver preconfigured predictive use cases to customers. Scores are consumed directly in the LOB application by the decision maker. The lifecycle of the models within the application is easily managed, include model retraining. Lengthy development cycles are avoided. Customers can seamlessly embedded more sophisticated models or create their own predictive use cases using SAP Predictive Analytics. 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 9

Predictive Analytics use cases for Utilities Моделирование и прогнозирование загрузки сети/потребления Прогнозирование отключений/перебоев/потерь Оптимизация технического и регламентного обслуживания Энерго-эффективность и аудит Анализ показаний «Смарт счетчиков» и выявление несоответствий Оптимизация резервирования Построение моделей для объяснения и прогноза условно постоянных и условно переменных потерь активной мощности для энергосистемы Моделирование зависимости потерь от: метеоданных, ремонтов, транзитных перетоков по сечениям, напряжения, напряжения в контрольных точках PUBLIC Прогнозирование цен на ЭЭ в рамках новой модели рынка

Проект в Энергосбытовой компании 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 11

Результаты применения SAP Predictive Analytics в ОАО Нефтедобывающая Компания 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 12

Прогнозирование и минимизация потерь Проект в Федеральной Сетевой Компании PUBLIC

Бизнес-кейс: Прогнозирование объема потребления электроэнергии клиентами ЭСК Предпосылки Краткосрочное (на 1-2 дня вперед) прогнозирование общего объема потребления э/э клиентами энергосбытовой компании (ЭСК) необходимо ЭСК для формирования заявок на покупку э/э на РСВ. Реализация Демо-пример был реализован на реальных данных ЭСК за 2010 год. В качестве исходных данных использовались исторические данные за 2010 год о почасовом потреблении э/э клиентами ЭСК, а также почасовые данные о погодных условиях в регионе обслуживания ЭСК. Необходимо было построить прогнозною модель, которая бы строила прогноз потребления э/э клиентами ЭСК на следующие 2 дня на основании исторических данных о потреблении э/э и на основе прогноза погоды на эти 2 дня. Результаты Прогнозная модель, построенная SAP PA, показала высокую точность прогнозирования. Средняя ошибка прогнозирования составила 1,41% при средней допустимой ошибке 3%. 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 14

Бизнес-кейс: исторические данные (обучающая выборка) 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 15

Бизнес-кейс: фактический объем потребления электроэнергии клиентами ЭСК за январь – август 2010 года 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC 16

Бизнес-кейс: результат прогноза – сравнение фактического и предсказанного объемов потребления э/э 1800 1750 1700 МВт*ч 1650 1600 1550 1500 1450 1400 1350 1300 Фактический объем э/э 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC Предсказанный объем э/э 17