GREEN TECH CENTER ÅBNINGSEVENT DEN 24. JUNI 2014 WORKSHOP 2 SMARTERE ENERGILØSNINGER VIA BIG ENERGIDATA

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1 GREEN TECH CENTER ÅBNINGSEVENT DEN 24. JUNI 2014 WORKSHOP 2 SMARTERE ENERGILØSNINGER VIA BIG ENERGIDATA

2 Anders Midtgaard General Manager INSERO Software A/S Thomas Hune Director of Energy INSERO Software A/S Mikkel Baun Kjærgaard Associate Professor ved Center for Smarte energiløsninger Mærsk Mc-Kinney Møller Institute Emil Holmegaard PhD Studerende ved Center for Smarte energiløsninger Mærsk Mc-Kinney Møller Institute

3 Baggrunden for Green Tech Centeret er omstillingen af samfundet til vedvarende energi.

4 Lean'Energy'Cluster'fokus' ' ' ' ' A'cleantech'ini+a+ve'in'the'field'of' energy'and'climate'technology' ' Work'to'promote'energy'efficiency,'based' on'sustainability'across'the'value'chain'of' the'en+re'energy'system.' ' Work'to'create'na+onal'as'well'as' interna+onal'cluster'ac+vi+es.' ' We'bring'together'companies,'public' authori+es'and'knowledge'ins+tu+ons'to' secure'growth'in'the'market'and'new' businesses.' ' Lean'Energy'Cluster'fokus'

5 Green Tech Eksport Hubs Nantong

6 Hvorfor Big Data

7 Mængde af devices og hastigheden på dataproduktion stiger voldsomt

8 I 2016 forventes verdens datacentre at håndtere 6.6 zettabytes pr. år Svarende til hver person på jorden streamer 2,5 timers HD video pr. dag

9 Vi har teknologier til at lagre alle disse data Udfordringen er at strukturere data, søge og finde information Det udfordrer vores traditionelle SQL teknologier Krav til Scalebility: Mængden af data Antal brugere Typer/format af data

10 Vore devices vil tale sammen Brugeren vil ikke vide hvilke data information er baseret på Informationerne vil komme fra forskellige services

11 Disse services vil IKKE blive leveret fra samme leverandør Stiller krav om åben kommunikation og adgang til fælles data Adgang til data vil skabe en ny platform for forretningsudvikling og nye services

12 e=player_embedded&v=oirxkhzuiiw Hvordan sikrer man kommunikation mellem markedsdeltagere

13 e=player_embedded&v=oirxkhzuiiw Hvordan sikrer man kommunikation mellem markedsdeltagere

14 Energisektoren har samme udfordring som ATM og standarder vil blive udviklet til en åben kommunikation e=player_embedded&v=oirxkhzuiiw

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16 Green Tech Centeret

17 Data fra Green Tech Centeret

18 Detaljeret view af data og kalkulerede værdier

19 Data fra Energilaug Vejle Nord

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21 Stor Skala legeplads for smart Grid løsninger VVM for 3-5 stk. 130 vindmøller p.t. 15 store virksomheder

22 Stor Skala legeplads for smart Grid løsninger p.t. tilsagn fra AAB

23 Landsby (Andkær) Stor Skala legeplads for smart Grid løsninger p.t. 50 familier

24 Data i dataplatformen i øjeblikket 15 industrivirksomheder Green Tech House Advice House 5 Testplatforme 20 En-familie huse 20 EL-biler Antal fysiske målepunkter ca. 700 (10 sek. data) Kalkulerede dataværdier ca. 400 Historiske data for 3 år for nogle industrier

25 Data i dataplatformen i nære fremtid 10 industrivirksomheder Green Tech House (BMS system) En-familie huse 40 lejligheder Antal fysiske målepunkter ca (10 sek. data) Kalkulerede dataværdier ca Historiske data for 3 år for alle huse/industrier

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27 Big data Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization. - Laney, 2012

28 Volume and Velocity Fra manuel til automatisk data indsamling i høj opløsning 1E+14 1E+12 1E Antal data punkter om året for en kontor bygning 1 Manuelt Smart Meters Sub Metering (0.02 Hz) Sub Metering (1 Hz) Fuldstændig Sub Metering (3600 Hz)

29 Vejrforhold Temperatur, Fugtighed, Vindstyrke, Vindretning, Solindstråling, Aktiviteter Menneskelig tilstedeværelse og adfærd Produktions aktiviteter Udstyr Andre typer af data Variety

30 Nye måder at behandle data på Big Data Machine Learning Information Visualisation

31 Information Visualisation Forbrug per blok i Los Angeles Vinter Sommer

32 Energi Effektivitet Mål: Forstå hvordan machine learning og informations visualisering kan hjælpe virksomheder med at optimere deres energiforbrug. Studie med virksomheder i energilauget. Interview om energi effektivitet Evaluering af tyve forskellige typer af visualisering af data

33 Eksempel: Advicehouse 5500 m2 Ca. 100 medarbejdere Elektricitetsmålinger I 2014 ( kwh)

34 Data i spil Virksomheder Kommune Borgere Organisationer Hvem har adgang / må bruge data og til hvad? Hvordan får man adgang til data?

35 Workshop

36 Problemstillinger Husholdningen Kommunen Virksomheden

37 Typer af Data Vejr Data Energi Data Adfærds Data Sociale Medier Green Tech Center Offentlige Data

38 Opgave: Fortæl en historie 3 grupper 30 minutter Lav en tegneserie der håndtere en problemstilling hvor der benyttes data fra Green Tech Centeret og Big data teknikker.

39 Vejle og Resilience

40 Resilience Hvordan kan big data data hjælpe? Fra Rockefeller fonden: Adaptive: changes based on new evidence Reflective: Learns from past experiences Robust: is organized & transparently managed

41 Examples of (Big)Data analysis in FINESCE Thomas Hune, Director, INSERO Software

42 Overview FINESCE (Future INtErnet Smart Utility ServiCEs) is the smart energy use case project of the 2 nd phase of Future Internet Public Private Partnership Programme (FI-PPP) funded by the European Union within FP7. 7 trial sites combining Smart Energy Solutions with Future Internet technology.

43 FINESCE Partners and Trial Sites trial site partner location

44 Agenda WP1 presentation from E.ON WP2 presentation from Insero WP2 presentation from SEnerCon

45 FINESCE WP1 FI providing the sustainable smart city energy COMPANY LOGO David Lillienberg

46 FI providing the sustainable smart city energy Scope Elaborate how Future Internet technologies can contribute to an efficient and robust Demand Side Management system Execute Demand Side Management and Demand Side Response tests with external buildings in Malmö, Sweden, based on an integrated approach of energy carriers Participants E.ON, RWTH Aachen

47 WP1 introduction Scope The scope of the WP1 trial is to execute Demand Side Management and Demand Side Response tests with external buildings Malmö, Sweden, based on an integrated approach of energy carriers Desired outcomes How Future Internet technologies can contribute to an efficient and robust Demand Side Management system Evaluate and test different business model(s) according to defined use cases to obtain better view on Demand Side Management and Demand Side Response as well as ideas on customer s potential to act as balancing power Scale-up strategy for the trial, e.g. ability for other towns/regions/business sectors to use the results/functionality Hyllie, Malmö Participants E.ON, RWTH Aachen WP leader: David Lillienberg, E.ON

48 WP1 Architecture Price data Weather data Generation data (CO2) GE prime candidates Data context group BigData Analysis Complex Event Processing Publish/Subscribe Broker Security and Access group Access Control Identity Management Platform Optimization Forecasting Load steering Baselining Command signals BMS Internet Command signals BMS / HEMS Command signals Possible future scope: - Interface to Meter Data Management - Interface to Distribution management system Heat loads El loads Heat loads El loads Commercial buildings Residential buildings Decentralized generation (possible future scope) - Large scale PV and Hyllie allocated wind turbines - Batteries

49 Use cases, GEs, APIs Use cases Cost optimization (electricity/heat) by price signals Optimization of demand (electricity/heat) by energy mix signals Instantaneous reduction of energy consumption GE prime candidates Big Data Complex Event Processing Publish/Subscribe Broker Access Control Identity Management APIs gettemperature: This method provides temperature forecast for the Hyllie district over a time interval getpowerprice: This method provides the Nord Pool power (electricity) price over a time interval getdistrictheatingprice: This method provides the district heating price over a time interval getdemand: This method provides the demand on load linked to the trial/demand response over a time interval

50 Big Data in WP1 Purpose Utilize the Generic Enabler BigData Analysis for processing large amount of data in order to validate optimization and find relevant insights concerning patterns and dependencies Accessible data Consumption Production Outside temperature Sun radiation Etc. Big Data CUSTOMER DISTRIBUTION PRODUCTION

51 Thank you! Telephone: +46(0)

52 FINESCE WP2 FI for end users of energy ecosystems Thomas Hune (INSERO Software) COMPANY LOGO

53 Goals WP 2 goal To test the mutual interaction of the technologies as well as the users experiences with the technologies. Furthermore, the coherence of the technologies with the entire energy system tested in an area outside the collective district heating.

54 Stenderup

55 WP 2 data Electricity consumption Indoor climate Electricity production EV charging and usage Heat production and consumption +40 data points per house Update frequency from 10 sec to 5 min

56 WP 2 data analysis - plans House modelling forecasting Forecasting indoor climate and heat consumption User behaviour seasonal changes Electricity consumption user models EV usage Driving patterns compared to normal car Charing battery usage range Finding and understanding flexibility Parameters influencing flexibility How much and when - forecasting

57 Impact of modernisation actions Multivariate big data analyses for consumption and modernisation events Dr. Johannes D. Hengstenberg Elmer Stöwer SEnerCon GmbH 06 14

58 Who is SEnerCon? Engineers, Programmers, Developers work on interactive web tools, web services and mobile apps Focus on Efficiency of Heating Systems Online Energy Advise for 1 million households per year iesa interactive Energy Savings Accounts monitor, display, analyse and benchmark meter readings of households Projects: Web Energy Performance Certificates, Climate Campaign (last campaign Heating reports), EU-Projects: ECCC, EPLACE, EECC, FINESCE. SEnerCon s FINESCE Team 67

59 What is the iesa? iesa: Since 2006 an interactive monitoring and energy savings advice tool for residential energy consumption Users have registered and collect manually 1,500 meter readings per day Technical equipment Base data of buildings Logging and Evaluation of modernisation events Univariate analysis: saving effects of green modernisation 68

60 What is the iesa? EAC - Energy Analysis from Consumption / HEMON - Heating Energy Monitor: Heating energy consumption is analysed with regression analysis over outside temperature Consumption signature of the building Automatic allocation of base energy for warm water, cooking etc heating energy Predicts annual consumption within short time Extended Benchmarking of heating energy consumption 69

61 Reduction in kwh/m2 Reduction in kwh/m2 Saving effects of green modernization Example Boiler replacement: 20 kwh per m 2 average reduction variation of kwh (!) Half of modernizations disappoint investors In modernization praxis and theory are not the same Challenges for our current Analysis Samples are much too small for multivariate analysis Buildings data not fully structured and centralized Data is fragmented and incomplete Low motivation of users to provide basic & event data Algorithms and databases are too slow for upscaling Replacement of boiler: specific consumption before and after kwh/(m 2 [AN]. a), weather adjusted , , N= Specific consumption before replacement Replacement of boiler with solar heating: reduction specific consumption in kwh/(m 2 [AN]. a), weather adjusted , Consumption before replacement 70

62 With big data analysis and FI-WARE we can have Permanent comparisons of consumption before and after modernizations Run multivariate analysis of all combinations of green modernization measurements Run cohort analyses to categorize building types, age, localizations etc Create consumption patterns based on geographical distribution Predict chances and returns of investments in single or combined modernization measures Predict economic and ecological impact of subsidies and building regulation Where should our money go? 71

63 Thank you! Elmer Stöwer SEnerCon GmbH Hochkirchstraße Berlin Telefon 030/ Fax 030/