state of the art in energy informatics – opportunities and ... · ctt: an iot-based carbon track...

07 October 2016 DTU Management Engineering, Danmarks Tekniske Universitet Add Presentation Title in Footer via ”Insert”; ”Header & Footer”

State of the art in Energy Informatics – opportunities and barriers

Alexander Tureczek, Alfred Heller, Xiufeng Liu,

Per S. Nielsen, Sara Ben Amer-Allam, Angreine Kewo

Presented at:

Scale UP Denmark Camp

6 March, 2017, Vejle

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Competences

Alexander Tureczek - Statistics and Data Analytics Alfred Heller - Building and IT Systems Angreine Kewo - Indonesian Energy system and Business models Per S. Nielsen - Indicator- and Smart Cities Development Sara Ben Amer-Allam - City scale energy systems and integration Xiufeng Liu - Data base systems and architecture

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What is energy informatics

Digitalization of society, amongst the energy sector. This generates lots of data that can be utilized in a societal and commercial way Methods are variant data analytics, artificial intelligence etc.

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Copenhagen 2003

Northeast black out 2003

Indien 2012

And many more

Why do we do it?

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http://visual.ly/future-internet-things


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Trends and Mega Trends

Increased communication and IT technologies everywhere. E-meter and sensors widely available and connected (IoT) Clouds that can collect big data sets and do necessary analysis on them > knowledge supported decision making.

Big data analytics (opportunities, uncertainties=barriers…)

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Challenges in the Danish energy sector

Slow implementation of e-meters and sensor technologies (?due to lack of competition?)

European Data Legislation makes utilization of data challenging An energy taxing structure which is not reflecting the need for flexibility (heat pumps, EVs, use of industrial waste heat) and favorises solutions such as biomass combustion

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Opportunities in the Danish energy sector

Strong political targets on CO2 reduction and introduction of renewable energy technologies as drivers

Highly skilled labor force An innovative business environment? Export opportunities (energy conversion): wind turbines, biomass combustion plants, waste-to-energy plants

Export of specific applications: pumps, thermostats,

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Energy informatics at the Danish energy market

Data based design and control of energy systems have been applied for a few years.

Due to data availability, a next step towards “intelligent” energy systems is ongoing.

Next step is an “integrated” approach of the following across sectors and systems.

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The power sector TSO/DSO

Energy Hub > what did/does it good for? DSM services and soon Flexibility services = aggregator services based on informatics.

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District heating companies

No Heat Hub - National collection by Skat is in “annual values” for all “users” - there is a need for hourly values and below

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Projects

•Energy Flexibility Smart Meter Analytics (SydEnergi) Some Demo Projects in CITIES Control of WWTP (ED, Krüger, ..) Heat pumps (Grundfos, ENFOR, ..) Supermarket cooling (Danfoss, TI, ..) Summerhouses (DC, SE, Energinet.dk, ..) Green Houses (NeoGrid, Danfoss, F.Fyn, ....) CHP (Dong Energy, FjernvarmeFyn, HOFOR, NEAS, ...) Industrial production (DI, ...) EV (charging) (Eurisco, ED, ...)

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Scientific Cloud Overview

SciCloud is a private cloud for scientific computing • It is running:

– With 18 physical servers – 80 cores – 1/5 TB of memory (RAM) – 12 TB storage (including backup storage)

• Real-time scalable resources provided “as a service (aaS)”

– Platform (PaaS) – Software (SaaS) – Analytics (AaaS)

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SciCloud Service - distributed computing environment

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SciCloud Service - Cloud-based data and analytics service • Cloud-based data management

• Open data portal • In-place data analytics service

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In-place data analytics in Jupyterhub

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CTT: An IoT-based carbon track and trace system

A holistic IoT solution for air quality monitoring: ● Open source software and hardware platform

● Scalable IoT data management solution

● Real-time dashboard

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IoT sensors for monitoring air quality

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IoT Data Management Solution

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Analytics and visualization

Model the correlation between traffic jam factor and CO2 21


Analytics

Data Science - Clustering - Machine learning

Mathematical modeling - Time Series models - Statistical models

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Popular methods

Metode Total

K-Means 22

hierarchical 10

fuzzy k-means 4

Follow-The_leader 3

Neural Network 2

K-medoid 2

Mixture Model 3

Fast Fourier Transform 1

KNN 1

Ant Colony 1

PC-Kmeans 1

Adaptive Vector Quantization 1

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Classification of 10 meters in 3 classes with K-means

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District Heating

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Signal analysis

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Alexander atur(at)dtu.dk

Data analytics Statistical analysis Customer classification Data Privacy

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Alfred alfh(at)byg.dtu.dk

Hardware solutions: Cloud, SciCloud, Data collection, monitoring, analytics Smart Campus, access Living Labs Lyngby Knowledge City

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Angreine ankewo(at)dtu.dk

Data analytics Market analysis Business models Techno-economic assessments of smart city solutions in emerging developing countries

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Per pernn(at)dtu.dk

Smart Campus, access to living labs Lyngby Knowledge City, IoT, Data collection, Techno-economic assessments of smart city solutions Marked analysis of smart city solutions Business models for smart city solutions

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Sara sbea(at)dtu.dk

Energy systems analysis using mathematical modelling (e.g. Balmorel, energyPRO, Sifre)

Policy analysis including taxation and legislation Sustainability assessments and business models for smart city solutions

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Xiufeng Lui xiuli(at)dtu.dk

•Data base systems •Data collection and DWH •Data Science

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Thanks for your time!

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