AWS Energy South Korea

Encored Technologies

provides a comprehensive solution for VPP sellers ranging from power generation prediction, floor control, and market bid optimization. It focuses on helping energy sellers create stable revenue in the VPP market by adopting AI-based technologies for power generation prediction and optimization.

Based on this, it performs calculations using up to about 4000 cores to predict power generation - a task only a high-end HPC cluster or a supercomputer could do in the past.
EVP Hyoseop Lee says building an on-premise datacenter with massive computing power usually takes at least 6 months plus the cost of configuration and operation, but it took only three days with AWS.
Encored Technologies saved cost by 60 percent after changing the instance to which is an ARM-based, HPC-dedicated instance type powered by AWS Graviton 3E processor.
The company loads and processes about 100TB of data on one go while creating the training data for its power generation forecasting AI model.

Reported impact

Business outcome

EVP Hyoseop Lee explained, “Nobody was sure if building the same system on an on-premise environment can give us the performance we want. Our researchers can directly configure the necessary HPC cluster environment as a code, which means we can remove or change clusters whenever we want.

  • Cost Reduction
  • Revenue Growth
  • Speed to Market

Solution

Encored Technologies is an energy IT company that uses AI technologies for renewable energy generation prediction and optimization so that it can help electricity sellers create stable revenue in the VPP (Virtual Power Plant) market. To this end, the company built an in-house Weather Research & Forecasting (WRF) model to improve prediction accuracy. Encored Technologies was able to accelerate the process of configuring a cost-optimized HPC cluster by building on Amazon Web Services (AWS) dedicated HPC instances on Amazon Elastic Compute Cloud (Amazon EC2)— Amazon EC2 Hpc6a and Amazon EC2 Hpc7g To generate weather forecasts, Encored Technologies carries out four calculations a day, with each calculation taking up to two hours using a cluster of 500 cores (maximum 4,000 cores daily), generating approximately 500GB of output every day.

Data flow

EVP Hyoseop Lee says building an on-premise datacenter with massive computing power usually takes at least 6 months plus the cost of configuration and operation, but it took only three days with AWS.

Solution architecture

5 components · 2 layers
  1. Compute
    • Amazon EC2 Hpc7g Runs cost-efficient ARM-based HPC workloads for large simulations and forecasts.
    • Amazon EC2 Provides elastic compute capacity for application, analytics, and infrastructure workloads.
    • Amazon EC2 Hpc6a Runs high-performance weather-modeling and simulation workloads.
  2. Storage
    • Amazon S3 Stores datasets, objects, model artifacts, or binary assets at scale.
    • Amazon FSx for Lustre Provides high-throughput shared file storage for HPC and analytics jobs.

Architecture clues

  • Encored Technologies built its initial HPC environment by creating an HPC cluster for weather forecasting using , an x86-based Amazon EC2 instance type for HPC.
  • Encored Technologies chose AWS’ HPC solution to save HPC environment configuration time and operate HPC clusters in a cost-efficient manner.
  • Encored Technologies saved cost by 60 percent after changing the instance to which is an ARM-based, HPC-dedicated instance type powered by AWS Graviton 3E processor.
  • It stored the big data required for weather forecasting using Amazon Simple Storage Service (Amazon S3) Amazon FSx for Lustre It used AWS ParallelCluster , an open source cluster management tool, for easy deployment and management.

Evidence from the source

Encored Technologies built an in-house weather forecasting system to improve the accuracy of power generation from renewables such as solar and wind.
It focuses on helping energy sellers create stable revenue in the VPP market by adopting AI-based technologies for power generation prediction and optimization.
The company loads and processes about 100TB of data on one go while creating the training data for its power generation forecasting AI model.
To generate weather forecasts, Encored Technologies carries out four calculations a day, with each calculation taking up to two hours using a cluster of 500 cores (maximum 4,000 cores daily), generating approximately 500GB of output every day.
provides a comprehensive solution for VPP sellers ranging from power generation prediction, floor control, and market bid optimization.
Transparency Evidence & methodology 91/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value91/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Encored Technologies customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/encored-technologies-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 29, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.