Smart Meter Data Insights solutionExperience insights-backed intelligent grid operations

ABC CorporationID No.: 01511291

8th January 2021

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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 ModernizationHead

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

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About the solutionSmart 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 o�-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 e�ciencies 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 e�ectiveness

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 e�cient

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 & ConnectedLogistics, 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

iotworks@hcl.com hcltech.com/IoT IoT WoRKSTM showcase