Few sectors have the scale and complexity as the energy and utilities industry and Hortonworks’ energy management solution leverages internet of things (IoT) big data for energy efficiency. Our 100% open-source connected data platforms enable energy data management and analytics for utilities with a single view of assets (we enhance grid visibility by orders of magnitude), operations and customers, predictive maintenance to prevent blackouts and leaks, and smart meter data analytics to detect outages and energy theft, and customer analytics to optimize customer service.
Power and utility companies around the globe face significant industry changes that are transforming how they conduct business. They must deliver safe, dependable, and affordable energy to their customers while also managing the pressures associated with increasingly stringent regulatory requirements, aging assets, rising demand for alternative energy sources, and management of energy big data generated from newly deployed Advanced Metering Infrastructures (AMI). Many of the world's largest power and utility companies have turned to Hortonworks' Connected Data Platforms to manage their data in motion and data at rest—improving reliability, operational efficiency, profitability, and customer satisfaction.
Smart Meter Data Analytics Improves Grid Reliability
Modern utility companies need to capture, transmit, analyze and store smart meter data in order to meet their business objectives associated with installing Advanced Metering Infrastructures. But their data architectures were designed during a simpler time when a monthly measurement was the norm. Smart meters collect information multiple times every hour, creating a vast, constant, and rich stream of data that utility companies are ill-equipped to process and store efficiently on legacy database platforms.
With 100% open-source Connected Data Platforms from Hortonworks, utilities enhance their grid visibility by orders of magnitude. Hortonworks’ energy big data management solution helps them monitor their data-in-motion from operated assets in real-time and compare that to deep historical analysis on past trends. That data discovery powers actionable intelligence for remote operations support, and also delivers real-time insights to: increase grid reliability, balance loads, reduce outages, and detect fraud.
A Single View of Assets to Optimize Grid Operations
Historically, power and utility companies’ operational technology (OT) and information technology (IT) systems have been developed, maintained, and used by siloed personnel within the organization. This reality has hindered cross-company collaboration and data visibility across business units, resulting in higher operating and energy costs, prolonged outages, inefficient operations, and poor customer service.
From the beginning, Apache™ Hadoop® was architected to combine data from many different sources with highly variable formats. Apache NiFi identifies all of those sources and moves them to a central location for storage and analysis (both in real-time and batch). Now Hortonworks offers both of those technologies in an integrated set of solutions for utilities data management and analytics. Connected Data Platforms integrate data from siloed operational, IT, and external systems, enabling OT/IT convergence to create new dashboards for a single view of assets. This cross-company visibility reduces downtime and optimizes grid operations, potentially saving millions of dollars.
Predictive Equipment Maintenance to Prevent Blackouts
Traditionally, operators gathered data on the health of generation, transmission, distribution and metering equipment through physical inspections. This meant that inspection data was sparse and difficult to access, particularly considering the high value of the hardware in question and the potential health, safety, and convenience impacts of equipment malfunctions.
Predictive maintenance helps utilities determine the condition of in-service equipment and then predict when maintenance should be performed. Rather than sending a maintenance truck based on the time of year, utilities now send them based on the actual need for repair. Hortonworks enables operators achieve energy efficiency from internet of things data by reducing failures and lower costs associated with routine or time-based preventive maintenance.
Single View of the Household for World-Class Customer Service
Utility companies built legacy data systems with a one-to-one relationship between the end application and storage platform. For example, the billing team manages a payment system with its database, the customer care team stores call logs in a CRM system, and the field service team stores data on service trucks and work orders.
Hortonworks’ data management and analytics solutions for utilities has helped some of the largest companies create a single view of their data and uncover value that might have been within reach, but scattered across multiple interactions, channels, groups and platforms. With that single view, they create customer personas, rank them by usage, optimize service calls, reduce churn and adjust target marketing for offers on value-added services like budget billing.
Energy Trading Intelligence, One Step Ahead of the Markets
Wholesale energy market participants face similar data challenges as utility operators, with an even more diverse set and volume of data sources that need to be collected, processed, stored, integrated and analyzed in order to reduce risk. Sources include sensor data from operated assets, market and exchange data, ERP data, data from trade and risk management platforms, and other internal and external sources.
Real-time trading solutions powered by Hortonworks enable energy traders to react instantaneously to market opportunities, without exposing their organizations to undue legal or financial risk. For example, one customer leverages our Connected Data Platforms to ingest, process, and analyze real-time electricity market data from a commodity exchange data service to enrich data in their existing trading platforms. This improves forecasting, allows them to identify market irregularities, and helps detect fraudulent trading practices.
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