a brief review of research on efficient energy systems at the ... · 15/11/2018 · bioh2:...

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19.11.18 Hochschule Landshut | Technologiezentrum Energie www.tz-energie.de

A Brief Review of Research on Efficient Energy Systems at the University of Applied Sciences Landshut, Germany

Prof. Dr. Markus U. MockUniversity of Applied Sciences, Landshut

Germany


19.11.18 Hochschule Landshut | Technologiezentrum Energie www.tz-energie.de 2

Research Focus Energy Efficiency

Collaborators & their Departments:

Prof. Dr. Alfons Haber Interdisciplinary StudiesProf. Dr. Sascha Hauke Interdisciplinary StudiesProf. Dr. Diana Hehenberger-Risse Interdisciplinary StudiesProf. Dr. Karl-Heinz Pettinger Interdisciplinary StudiesProf. Dr. Josef Hofmann Mechanical EngineeringProf. Dr. Tim Rödiger Mechanical EngineeringProf. Dr. Stefan-Alexander Arlt Electrical EngineeringProf. Dr. Markus U. Mock Computer Science

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19.11.18 Hochschule Landshut | Technologiezentrum Energie

www.tz-energie.de 3

In Lieu of a Motivation Slide

■ “Energiewende” = Energy Transition

■ 2011 in response to the Fukushima nuclear accident

■ Goals for Germany to achieve by 2050

■ 80% of electricity comes from renewable sources (e.g. wind, solar)

■ At a minimum 60%

■ Reduce primary energy consumption by 50% (compared to 2008)

■ Reduce greenhouse gas emissions by by 80-95% compared to 1990



Energy Transition Status

Since 2014: actually going up…



Energy Related Research in our Region



Landshut

City of Landshut (founded in 1204)

Trausnitz Castle

St. Martin‘s Church

Landshut Royal Wedding



Regional Industries

■ Premium car manufacturers

■ Subsystem suppliers

Landshut is a region where many industries are booming, such as automotive, energy, life science and many more



The TZE in Numbers

§ > 5 Mio. € Investment

§ 700 m2 office space

§ 1.000 m2 lab space (11 labs)

§ ca. 20 employees

§ ca. 10 parallel projects

§ > 7 Mio. € research projects volume at the TZE as of June 2018Formatvorlage des Untertitelmasters durch klicken bearbeiten


The TZE in Numbers

§ > 5 Mio. € Investment

§ 700 m2 office space

§ 1.000 m2 lab space (11 labs)

§ ca. 20 employees

§ ca. 10 parallel projects

§ > 7 Mio. € research projects volume at the TZE as of June 2018



And in Google Maps



Setup of teaching and research facilities



Ressources

Technology Centre Energy



Ausbau des TZE

Ground Floor Lab Extension



Agenda for the Talk

■ Introduction■Overview of Research projects■Deep dive into Non-Intrusive Load Monitoring (NILM)■Summary



Energy Storage

■ Important building block for the integration of wind and solar energy into the energy mix

■Multiple ongoing projects (PIs Prof. Pettinger & Rödiger)■CompStor, EKOSTORE & FSTORE,

■ Building a competence center around energy storage (education & technology transfer)

■Cooperation with University of App. Science Upper Austria & Pilsen, Czech Republic


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www.tz-energie.de 18

Energy Systems SimulationResearch Topics :

• Research on decentralized energy systems

(< 30 kWel) with respect to integration of

electrical and thermal energy storage systems

• Systems analysis / optimization, model

development and investigation of transient

processes

• Innovative energy management systems and

control systems optimization

• Modelling of grid integration aspects of

decentralized energy systems

• Design of test rig and demonstration plant of

hybrid systems (with MCHP micro-combined

heat and power systems)

Technology Centre Energy



Energy Systems Simulation

■ Experiences in system simulation at the TCE within the Project EKOSTORE / FSTORE

■ Development of new CHP control strategies in systems with EES

■ Energy and power based black / grey Box Modelling for system components

■ Scenarios defined from single to multi family houses up to 17 apartments



Energy Systems Lab



Results of the simulations – Quantify the performance of the system (1/2)

Abbreviations: ESF: Energy storage-following control strategyTLF: thermal load-following control strategy



Results of the simulations – Quantify the performance of the system (2/2)

Abbreviations: ESF: Energy storage-following control strategyTLF: thermal load-following controly strategy



■ Detailed electric system analysis for an exemplary cloudy spring/ autumn day utilising a electric storage-following control strategy

■ Electric energy normalised based on the daily peak electric demand

Thermal and electric system analysis for an exemplary day



Varying the component size

■ Analysis for varying the size of the EES capacity and/ or the electric PV power output

■ Degree of self-sufficiency when varying component size for a (a) thermal load-following and (b) electric storage-following control strategy

■ Determine the best (?) system combination for each case



Current work:Integrate a model of a VRFB in the existing system simulation

■ Battery model in the EKOSTORE project is based on a power and energy based black-box model for lithium-ion batteries

■ Specified parameters used for the linear battery model■ Minimum/ maximum EES energy capacity■ Constant discharging/ charging efficiency■ Discharging/ charging power■ Converter efficiency defined by electrical Power and DC-Voltage of EES

■ Approximation of DC-Voltage of EES by SOC

VRFB = Vanadium Redox Flow Battery



Next steps for the VRFB system approach

■ Interface definition for a combination of existing simulation approaches from the EKOSTORE project and a detailed model of the VRFB

■ System analysis with a VRFB model and the electric and thermal load profiles in the defined scenarios

■ System analysis without combined heat an power analysis is possible■ Further studies should also consider different load profiles e.g. for

small and medium sized industries or hotels■ Development of a VRFB model in combination with the existing system

approach could be used for a collaborative publication■ Definition of economic costs for a VRFB in such system combinations

is possible



Other Battery-Related Research

■ LoCoTrop: Low cost dry coating for battery electrodes■Surfalib: better Lithium-Ion batteries by improved electrode

coatings■KME-2nd Life



Other Projects

■BioH2: microbiological production of hydrogen from biomass (Hofmann)

■NHEAT: ultrafast measurement of heat flows (Rödiger) ■And others..



• No thermal disturbances created in the boundary layer

• Combination of high heat-load durability and wide spectral resolution

Introduction – Atomic Layer Thermopile (ALTP)IAG Institute ofodynamics und Gas Dynamics

• ALTP signal direct proportional to wall heat flux• Linearity of signal over 10 orders of magnitude (10 µW/cm2 – 20 kW/cm2)• Spectral resolution up the 1MHz range

Þ No interference in array measurements

Þ Suitable for high-enthalpy flow environments Þ Total-temperature probe



Agenda for the Talk

■ Introduction■Overview of Research projects■Deep dive into Non-Intrusive Load Monitoring (NILM)■Summary



Deep Dive: Non-Intrusive Load Monitoring

From aggregate energy use -> individual components, aka. Energy Disaggregation or NILM



Why is this hard?

■ An example of a single-channel blind source separation (BSS) problem■ we want to extract more than one source from a single observation.

■ Several sources of uncertainty■ noise in the data■ lack of knowledge of the true power usage for some appliances in a given

household■ multiple devices exhibiting similar power consumption■ simultaneous switching on/off of multiple devices.



Why is this useful?

Source: eSource



Applying Machine Learning to NILM:Experimental Setups

■Supervised techniques■Appliance level data is available for the household for which

NILM is performed

■Partially blind techniques with appliance list available■Appliance level data from other houses and list of appliances

in particular household are available

■Completely blind techniques■Examples of appliance level data available but nothing

known about what target appliances are present


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www.tz-energie.de 35

Data Sources

• We used Dataport, the largest data set available to us

• In the future: working with EON (large German energy

company)



NILM Prior Approaches

■Factorial Hidden Markov Model (FHMM) ■ Extension of a HMM: each appliance is modelled by a HMM■ Has been used in many prior approaches

■ A HMM has two components■ Observed variables (model appliance state, on, off, standby etc.)■ Hidden variables (electrical usage)

■ Each state is a probability distribution■ Learning Problem: learn parameters of the FHMM model

parameters (optimization problem)



Markov Models



NILM with Deep Learning

■Deep Learning = artificial neural networks■ Training = finding the parameters of the neural network that

minimize prediction error■Making it practical

■ Input and output sequences are typically very long■Makes training of models computationally expensive and runs

into memory problems

■Solution:■Divide sequence into chunks that are processed one-by-one



Processing Pipeline



Sliding Windows

• Chunks are broken into windows• Training is done with a full window• Predicted values are taken as the mean of the values predicted• Windows are applied both sequentially and randomly



Neural Network Architectures

■ We used two architectures■ RNN (Recurrent Neural Network)■ CNN (Convolutional Neural Network)

■ For each possible appliance we predict the amount of power consumed at any particular point in time

■ Mean square error as error function in training, mean absolute error in evaluation

■ Both for supervised and unsupervised scenarios, showing latter..



Initial Results: Unsupervised Learning

ID Appliance Prediction Model MAEB1 Fridge FHMM 71.84E7 Fridge CNN 25.18E10 Fridge RNN 18.40E8 Washer CNN 4.02

• Training was done with 3 training houses• Evaluation (test) on a different test house



Fridge: Unsupervised



Conclusions

■ Both neural network types perform significantly better than Hidden Markov Models

■ RNN slightly better than convolutional network■ Takes longer to train (ca. 12 hours on a standard laptop, no GPU)

■ Training models is computationally feasible■ Lots of speedups to be had with faster hardware

■ Other researchers have found similar encouraging results, e.g. Jack Kelly Imperial College (Ph.D. thesis 2015) and Zhang et al (AAAI 2018)



Ongoing and Future Work

■ More thorough and long-term evaluation of NILM ML predictions■ E.g. reducing ”training loss”

■ Integrating ML the HAW Landshut Energy Management System



Questions?

■ Feel free to reach out to [email protected] or [email protected]

mailto:[email protected]

mailto:[email protected]

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