Smart Meter Data Insights solution
Experience insights-backed intelligent grid operations
ABC Corporation ID No.: 01511291
8th January 2021
Remaining 53.46 kWh
84.05kWh 0.0 kWh
BDT 346.57
Usage details kWh
Calculater Fault &
Detection Account Details
Month wise
Details
Current Update
The adoption of smart meters is increasing, globally…
Smart meters and Meter Data Management Systems (MDMS) rollouts are almost a decade old. With utilities transitioning rapidly towards smarter, insights-led operations, nearly 67% of the 1.6 Bn meters distributed between 2018 and 2025 are going to be smart meters – driven by large scale EU, MEA, ASEAN and APAC rollout plans. The evolution of the smart grid is being driven by increased demands for consistent service uptimes and energy sustainability goals of utilities organizations, resulting in a full blown transition to smart meters – resulting in a 59% projected penetration of smart meters globally, by 2028. Real-time utilities asset health analytics and consumption behavior tracking can enable organizations to optimize their grid repair, maintenance and operations costs, as well as reducing usage through incentives that aim to optimize consumption patterns – underlining the potential of the data derived from smart meters.
EU’s Third Energy Package target of 80% smart metering coverage by 2020
India’s smart metering program to replace 250Mn conventional meters with smart meters
Connected Homes worth 135.3 USD Bn by 2025 (p) – driving up the adoption of smart meters
Smart-metering data reveal key usage patterns that can then be lever- aged for better end customer experience
…but are utilities truly able to generate actionable insights from the data?
Typically, energy consumption data from smart meter devices are collected and sent to customer information systems (CIS) for billing and processing.
While this helps with higher visibility and accuracy in consumption data – the true potential of MDMS (Metering Data Management Systems) remains unexplored. Decarbonization and decentralization initiatives can benefit from unlocking hidden value in metering data by enriching it with GIS, CIS and weather data. Potentially, asset health monitoring, theft and fraud detection, energy usage analyses, detecting system anomalies and outages, are among the many use cases that can be built on top of AI/ML processed insights – ultimately holding the key to ensuring consistency in service uptimes, end customer satisfaction, and sustainable and optimized operations and costs.
0 2 3 8 2 3
Meter consumption
Sources: Smart Energy, Smart Energy GB, ESI-Africa, Markets and Markets,
Key drivers that necessitate insights from smart metering data:
Key Stakeholders
Interval Meter data can provide rich insights on consumption and grip operations
Global smart grid market (est. USD 28.8 Bn p2021) is estimated to grow at 25%
(2024p)
Planning new
‘behind the meter’ initiatives EVs, Roof top PVs, Solar batteries &
dispatchable generation
Globally, the big data analytics market is estim- ated to grow 4.5 times, garnering revenue of 68.09 USD Bn (2025 p)
Increasing tamper detec- tion costs when false alarms on site require field visits & ops costs
Distribution System Operations Head
Customer Experience and Operations Head
Grid Modernization
Head Customer
Care Heads Digital
Transformation Head
T&D Operations
Head Analytics/Big
Data Head Asset &
Equipment Head
Power distribution utility customers and power sector research organizations
Organizations
Sources: PRNewswire, businesswire d
About the solution
Smart Meter Data Insights (SMDI) solution by IoT WoRKSTM leverages our partner Onesait’s Meter Data Analytics (MDA) capabilities to extract contextual and key smart meter data from MDMSes – including OT data from SCADA/GIS/DMSes in order to analyze and provide a comprehensive list of powerful, detailed dashboards for real-time tracking of usage patterns, cluster analyses, load analyses and theft detection, among others – decoding
consumption behavior, enabling better management of utilities assets and driving transformation of grid operations.
SMDI solution utilizes open source tools and centralized semantic models to create a logical data acquisition layer. Raw and
processed data is stored in various open-source stores – it leverages AI/ML models, data mining, web-based notebooks and cognitive libraries to process and produce rich,
powerful and actionable meter data insights. These insights are then made available on a series of dashboards
displaying load analyses, demand forecasting, cluster analyses, anomaly detection and other information.
Dynamic Dashboards: Web-based dashboards displaying smart metering KPIs leveraging real-time and historical data – can trigger alerts in case of deviations from defined conditions
Powerful Toolsets: Rich set of applications covering data ingestion, storage, transformation, analysis and output visualization
Pre-build AI/ML models: Pre-built algorithms to build meter analytics solutions such as voltage analysis, theft detection and clustering analysis among others
Integrated Governance: Enables integrated governance of elements that make up the platform - integration with enterprise systems such as DMS, GIS, Work Management etc.
to leverage value
Solution Features
Real-time Convergence: IT/OT based on logical models (Ontology) for semantic integration. The metering data model describes the meaning of entities, relationships, and data
's
Theft Detection: Allows determination of customers that have energized themselves, were mistakenly energized or do not have proper order for energization, including if power is being sourced illegally
Site usage analysis – Provides analytics and AMI data to identify material changes in a site's electricity usage. Provision to apply thresholds against month on month consumption, sequential or prior year, to identify outliers
Proactive identification of operational issues – Enables analysis of AMI interval data (load, voltage), momentary interruption data, measured and calculated data from ADMS to locate potential faults that if not corrected would lead to sustained outages - including loose connectors, neutral faults, high-impedance faults, intermittent vegetation contact
Key use cases
Load Profile
Analysis Transformer
Analysis Cluster
Analysis Forecast Analysis
Forecast Deviation Analysis
Provides load forecast devia- tion analysis and checks robust- ness of predic- tions at the feeder and circuit level
ESurplus/
Storage, error history, deviation comparison, error distribution Provides insight
into the load forecast time series (24 hours forecast) at feeder and circuit levels
Historic &
forecast, correlation variable, historic consumption Provides insight
into load time series patterns and groups points into clusters that have a similar hourly load curve Cluster compari- son and profile Allows reviewing
of transformer overloading and performance statistics via aggregation of power entities captured at downstream customer meters Anomaly distribution, quantity, historic
& forecast percentage Provides insights
into load time series. The analysis is provided at the transformer, feeder and circuit levels. The dashboards cover various KPIs for in-depth analysis of load profiles.
Peak and off-peak energy consumption &
temperature etc.
Solution Benefits
Utilize analytical, operational and process intelligence to accelerate smart metering analytics use cases using prebuilt solutions, reduce time to market
Generate Intelligence- based Value
Based on CaaS technologies and containers, introduce operational simplicity under unified console.
Balance compute capacity and storage across Cloud and devices
Monitor asset health and predict downtimes, cascade older, less used assets to highly loaded areas to improve reliability and minimize outages Enhance Flexibility Improve Service
Reliability
Leverage insights and KPIs based on
AI/ML-driven models to optimize operations, improve efficiencies and bring in cost savings, introduce interoperability and self-discovery
Streamline Operations
Scale rapidly, enable development of solutions securely. “Think Big, Start Small”. Bring agility in the application of latest technologies in a cohesive way
Built on an open-source platform which makes the most of the capabilities of any vendor, avoiding "vendor lock-in“
Provides multi-platform support include public cloud, private cloud and on-premise platforms Flexible pricing depending on needs: on-premise, as a cloud service, by module, with or without infrastructure, with support and without risk
Introduce Robustness, Scalability
Solution Differentiators
Creating value through integration with Microsoft Azure, AWS & Dell
Analyst Recognitions
IoT WoRKSTM is a dedicated IoT business unit of HCL Technologies. Our award winning,
best-in-class, customer and industry specific, deployment ready solutions co-created with customers, enable them to maximize effectiveness and returns on their asset investments.
Rated as a global leader in IoT consulting & services by top analysts, our solutions, enable IoT-led business transformation through creation of more efficient business processes, new revenue streams and business models that deliver measurable business outcomes.
At HCL we believe that the transformative impact of IoT is realized by IoTizing the ‘things’, connecting the assets to a data platform.
Who we are
LEADER
IDC Marketscape, IoT Consulting
and Systems Integration Services,
2020
IDC
LEADER
Zinnov Zones for Connected Assets & Connected
Logistics, 2019
Zinnov
LEADER
ISG Provider LensTM for IoT managed services, USA 2019
ISG
LEADER
ISG Provider LensTM for IoT consulting and services, USA
2019
ISG
LEADER
ISG Provider LensTM for IoT in Manufacturing,
USA 2019
ISG
[email protected] hcltech.com/IoT IoT WoRKSTM showcase
