Sharing our work on optimizing PV energy yield leveraging IIoT, serverless framework, Elasticsearch and numerous open source tools with Los Angeles' Elastic User Group

1. hybriData©2019HybridDataSolutions,LosAngeles,CA
CJ Ejimuda
Optimizing PV Energy Yield
with Elasticsearch and
graphQL

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● Objective
● Overview of PhotoVoltaic (PV) systems
● Technology stack
- Elasticsearch - “You Know, for Search!”
- graphQL(AppSync)
- Serverless
- AWS
● Elasticsearch and IIoT data
● Conclusion
Agenda
©2019 Hybrid Data Solutions, Los Angeles, CA

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● How you can optimize PV energy yield from IIoT data leveraging
Elasticsearch and numerous open source tools
Objective
©2019 Hybrid Data Solutions, Los Angeles, CA

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Overview
Source:https://solarprofessional.com/articles/design-installation/deploying-solar-plus-storage-microgrids/page/0/3#.XMe3UJNKhZI
● PhotoVoltaic systems convert sunlight (Photo) into electrical power (Voltaic)

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Distributed Energy Resources
Overview

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● Use of 3rd party weather data
● Unreliable estimates of PV system Energy Performance Index
and PV Energy Yield
Challenges
©2019 Hybrid Data Solutions, Los Angeles, CA

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● Weather climate features: Cloud Coverage, dew point, wind speed,
station pressure, temperature, relative humidity
● Forecast using sensor data values of IIoT device at PV system
location
Solution
©2019 Hybrid Data Solutions, Los Angeles, CA

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AWS IoT
Core
Elasticsearch
Kinesis
S3
S3
Lambda
DynamoDB
AppSync
- graphQL
- Apache VTL
React Native
Technology Stack
DynamoDB
ReduxTemperature
Humidity
Gateway
Sensors

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Serverless Stack©2019HybridDataSolutions,LosAngeles,CA
● Infrastructure as Code - AWS CloudFormation yaml templates
● Deploy with command: serverless deploy -s dev -v

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Elasticsearch Stack©2019HybridDataSolutions,LosAngeles,CA

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● Schema
● Queries
● Mutations
● Subscriptions
● Resolvers
AWS Appsync - graphQL
©2019 Hybrid Data Solutions, Los Angeles, CA

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AWS Appsync - graphQL

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AWS Appsync - Apache VTL©2019HybridDataSolutions,LosAngeles,CA
[
#foreach($entry in $context.result.hits.hits)
#if( $velocityCount > 1 ) , #end
$utils.toJson($entry.get("_source"))
#end
]
{
"version":"2017-02-28",
"operation":"POST",
"path":"/sensor_data/_search",
"params":{
"body": $context.arguments.query
}
}
Query.search.request.vtl
Query.search.response.vtl

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serverless-appsync-plugin©2019HybridDataSolutions,LosAngeles,CA

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Kibana / Canvas©2019HybridDataSolutions,LosAngeles,CA

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● Proactive alerts
● Reliable Energy Performance Index
● Accurate PV Energy Yield
● Demand Response
● Planning and Commissioning
ES and IIoT data
©2019 Hybrid Data Solutions, Los Angeles, CA

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● IIoT environmental data would forecast PV energy
yield more accurately than 3rd party weather data
● Numerous variables impact the output of PV energy
yield:
- Power generation system components
- Other climate data
Conclusion
©2019 Hybrid Data Solutions, Los Angeles, CA

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Thank you!
©2019 Hybrid Data Solutions, Los Angeles, CA

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Questions?
email: cj@hybridata.us
©2019 Hybrid Data Solutions, Los Angeles, CA

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Back Up
©2019 Hybrid Data Solutions, Los Angeles, CA

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