Towards a Smart City Playground for Research and Experimentation

of Energy Use Cases in a University Campus Setting

Nikolay Tcholtchev, Martin Gergeleit, Stephan Böhm, Heinz Werntges

and Holger Hünemohr

RheinMain University of Applied Sciences, Wiesbaden, Germany

Keywords: Energy Management, AI, Data Platform, 5G, IoT, Smart City, Open Urban Platform, Experimentation.

Abstract: The current paper describes our planned activities towards establishing an experimentation platform for

energy management as part of our research and teaching activities at the RheinMain University of Applied

Sciences (UAS) in Wiesbaden, Germany. Our vision is to establish a data platform that gathers energy

related data and information from various sources, including sensors, weather data, existing energy

management systems, open data platforms and different research data sets available for the community. In

order to transfer the data to the data platform, different communication protocols (e.g. MQTT, 5G/6G,

LoRaWAN, WiFi, CoAP, 6LoWPAN …) and data models/formats (NGSI-LD, XML, JSON, SensorThings

…) should be put in place and utilized in a campus setting, such that data and information can flow into the

logically centralized data platform. Our vision is to follow established smart city (pre-)standards for Open

Urban Platforms, such as the DIN SPEC 91357, DIN SPEC 91397 and DIN SPEC 91377. By following

such open modular approaches, we want to enable a framework of different software and hardware

components as well as datasets, which can be used for teaching (e.g. in seminars and lectures) as well as for

research activities in the course of PhD projects.

1 INTRODUCTION

The recent years were marked by intensive

developments in the areas of Smart City, Smart

Country and Smart Region. All across the world,

one can observe the increased introduction of smart

digital solutions in public spaces and at the interface

between administration and citizens (IMD, 2024)

(BITKOM, 2024).

The core of the Smart City/Region in this context

is constituted by the use of information and

communication technology (ICT) to collect data

about a city/region and derive intelligent (real-time)

decisions and measures. These decisions can either

happen on city/region management level or on

personal level, in cases like personal energy

consumption and mobility. This vision has been

facilitated by the fast recent developments in the

areas of telecommunications, distributed systems,

the Internet of Things, cyber-physical systems, big

data and artificial intelligence. In this context, one

can observe the following key enablers:

• Enabler 1 – all-IP: The establishment of the

Internet Protocol (IP) and the Internet as the all-

encompassing communication medium with

connections to sensors, control systems, public

data and many other types of information and

objects has made it possible to merge the virtual

and physical worlds in line with the notion of

cyber-physical systems (Mikusz et. al., 2014).

This interaction between physical and digital

solutions facilitates that (real-time) data about

the (urban) environment and developments in the

city/region is transmitted to corresponding data

platforms for further analysis (Schieferdecker et.

al., 2016) (Tcholtchev et. al., 2021)

(Schieferdecker, Bruns et. al., 2019) (Tcholtchev,

Lämmel et. al., 2018).

• Enabler 2 - XG: The rapid development of

mobile network architectures (4G/5G/6G), which

is to a large extend enabled through the

utilization of IP technology, offers the possibility

for the (dynamic) placement of various sensors

and actuators in the smart city and region.

• Enabler 3 – IoT: Additional possible

complementary technologies in this context are

given by various IoT devices and belonging

wireless communication protocols, such as WiFi,

Tcholtchev, N., Gergeleit, M., Böhm, S., Werntges, H. and Hünemohr, H.

Towards a Smart City Playground for Research and Experimentation of Energy Use Cases in a University Campus Setting.

DOI: 10.5220/0013495900003953

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 14th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2025), pages 205-214

ISBN: 978-989-758-751-1; ISSN: 2184-4968

205

LoRaWAN, IEEE 802.15.4 and 6LoWPAN, to

mention a few.

• Enabler 4 – AI/ML: Advances in artificial

intelligence and machine learning, which were

unimaginable some years ago, enable efficient

analysis, pattern recognition and measure

identification with the aim of improving the

quality of life and optimizing processes in an

intelligent (urban) environment.

Figure 1: Simplified Illustration of the DIN OUP ICT

Reference Architecture for Smart Cities (Tcholtchev et.

al., 2021) (Schieferdecker, Bruns et. al., 2019)

(Tcholtchev, Lämmel et. al., 2018).

Enabler 5 – Big Data: A smart city/region is built

around an (urban) data platform that brings together

different data sources in an urban ecosystem. The

data sources can be versatile and include both static

data (e.g. government data, open data in general, and

any kind of urban data and information whose

value/parameters are not constantly changing) and

dynamic data (like continuous real-time data such as

sensor/Internet-of-Things data, global positioning

information …).

To enable the above drafted approach, different

components, network segments and computing

nodes from various silos and domains need to work

together efficiently to enable a data-driven Smart

City/Region. This type of architectural Smart

City/Region approach can also be applied to the

energy domain, especially in the increasingly

importance-gaining demand-response model in the

scope of renewable energy transition settings as

implied by the EU Green Deal and the “Energie-

wende” in Germany. The basic idea is to break the

energy data silos and to compile a logically

centralized one-stop shop for various energy related

data streams and datasets, in order to facilitate the

AI based analysis and interplay of cross-domain use

cases and solutions towards the end user.

In order to enable the above setting, we plan to

develop an integrative architecture according to the

principles of Open Urban Platforms (i.e. DIN SPEC

91357) thereby mapping and identifying key

technologies to the main pillars and layers of

reference structure. Within this architecture,

different types of energy related data and sensors

should flow together, such that AI based analysis

and correlations of datasets are enabled. In this

setting, we envision to teach and research in the

scope/context/environment of the RheinMain

University campus.

The rest of this paper is organized as follows:

Section 2 presents related work. Section 3 gives an

introduction to the concept of Open Urban Platforms

(OUP) and relevant German DIN standards. The

following section 4 identifies and exemplifies key

technologies and components fitting into the planned

energy related OUP, while section 5 puts the pieces

together and outlines our technological blueprint.

Section 6 and 7 discuss the next steps and draw

appropriate conclusions.

2 RELATED WORK

The utilization of Open Urban Platforms and

collaborative Smart City data approaches has been

researched in many projects during the past years.

The WindNode project (Graebig, 2017) on German

national level has developed various solutions and

simulations for collaborative demand-response

energy load balancing (Boerger, Lämmel, et. al.,

2022) (Rangelov, Subudhi, et. al., 2021) (Rangelov,

Boerger, et. al., 2023), including blockchain-based

solutions and centralized open data platforms. Smart

City EU projects such as Triangulum (Fernandez et.

al., 2023) and RemoUrban (de Torre et. al., 2021), in

which energy use cases were considered as an area

for smart city development viewing data platforms as

a facilitator. It is important to remark that the

mentioned projects are just examples for the large

number of activities executed within the last decade

in the domain.

In addition, solid and innovative research was

conducted on the topic of digitalization of energy

infrastructure and markets, with (Baidya et. al., 2021)

(Di Silvestre et. al., 2018) and (Zein et.al., 2024)

being some relevant examples. Especially, the

problem of asynchronous demand-response energy

production and generation has been widely discussed

in literature and in the scope of introducing

renewable energy sources (Rangelov, Boerger, et. al.,

2023) (Akhter et. al., 2019). Thereby, methods from

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206

the domains of AI and Machine Learning have been

extensively applied, in order to align the energy

demand and generation predictions and to increase

the efficiency of the overall system.

The aspects of cybersecurity and ICT

management of the digital infrastructure are also

crucial for the success of such use cases, approached

through the concept of Open Urban Platforms. In this

context, various research activities address the

domains of penetration testing (Zhukabayeva et. al.,

2024), risk assessment (Lupton et. al., 2022),

vulnerability assessment (Tariq et.al., 2020) and the

overall establishment of security operation centers

for such critical infrastructures (Mohammad, 2019).

Hence, we hope that with our envisioned

experimentation platform future engineers in the

domain will be also trained and sensitized regarding

the imminent threats in this area of digitization.

3 OPEN URBAN PLATFORMS

The Smart City/Region introduction of critical digital

solutions is of particular importance, because the

associated ICT components are becoming

increasingly relevant as the technical backbone of

digital societies in cities and municipalities. On the

one hand, it is of the utmost importance that these

ICT infrastructures are of particular quality and

reliability - e.g. secured by intensive quality

measures and protection against cyber-attacks. At the

same time, it is essential that potential dependencies

of the municipal and administrative infrastructure on

individual providers/manufacturers are avoided, in

order to ensure a higher level of digital and

technological sovereignty. This technological

sovereignty is made possible by the establishment

and use of standards for the ICT infrastructure in

smart cities/regions. Recent years have been

characterized by the introduction of a number of

bodies working on standards in the field - e.g.

(ISO/CD 37173) (ISO/DIS 37170) (ISO/DTS 37172)

and (ISO 37166:2022) as well as cooperation within

initiatives such as FI-WARE (Torrepadula et. al,

2024), Living-in.eu (Living-in.EU, 2024) and Open

Agile Smart Cities (OASC) (OASC, 2024). For

Germany and Europe, the activities within the DIN

standardization body are of particular importance,

with the DIN SPEC 913X7 series as the main pillar

of standardization for Smart City/Region ICT in

Germany (DIN DKE Smart City, 2024). The DIN

SPEC 913X7 series consists of various specifications

that describe key aspects and use cases. DIN SPECs

have a special role in the standardization landscape

by constituting a kind of pre-standard, which is

particularly suitable for dynamic new areas such as

ICT, in order to publish current results in the short

term and make it possible to update them in 2-3

years, while in parallel they can be put on the path to

international standardization at CEN/CENELEC or

ISO. The main specifications of the DIN SPEC

913X7 series are briefly presented below.

DIN SPEC 91357 (Heuser et. al., 2017): This DIN

SPEC defines a reference architecture for Open

Urban Platforms. In this context, it is assumed that

the main difference between a city/municipality and

classical organizations (e.g. companies) is that

municipalities are not monolithic entities and even

the administration - as an interface and service

provider for citizens - is only part of a much larger

ecosystem in the urban and municipal context.

Therefore, a collaborative approach involving the

various local actors, stakeholders and organizations is

required to provide efficient and beneficial digital

solutions and infrastructure. In this context,

municipal data is a resource and basis for a number

of use cases, e.g. mobility, energy optimization and

public safety. The challenge for German (and

European) cities/municipalities/regions is to identify

and acquire scalable solutions that can be adapted to

their needs and to draw from a variety of potential

services/components/providers that can also be

provided by local providers, SMEs, academia, open-

source initiatives, and start-ups. The key question

here is how these services and data are organized,

managed and provided. Municipalities therefore need

an architectural digital framework in the form of an

“urban platform” that brings together all the

different services and integrates the resulting data.

Such an abstract reference architecture for Open

Urban Platforms (OUP) is provided within the

framework of DIN SPEC 91357 and illustrated in

Figure 1.

The DIN OUP ICT reference architecture consists

of eight layers and two pillars. Each layer/pillar is

characterized by a series of capabilities, which are

logically placed/located within this framework. The

bottom layer 0 in Figure 1 represents the data sources

within a municipality/city/region. Various sensors

and measuring stations are located there, which

generate data for intelligent urban development and

management. Layer 1, which builds on this, is

responsible for networking individual devices to a

communication infrastructure, e.g. via a

telecommunications network or the Internet. The

devices and sensors in the layer with the data sources

as well as the communication infrastructure are

controlled via protocols and components that are

logically assigned to the layer called “2. Device Asset

Management & Operational Services Capabilities”.

Based on this basic infrastructure, the data from data

Towards a Smart City Playground for Research and Experimentation of Energy Use Cases in a University Campus Setting

207

sources is fed into the data platforms in the third

layer above. This includes, for example, databases,

open data portals, cloud platforms, event-based data

processing systems and metadata catalogues. The

data from the sources is prepared and offered for

further services and applications in the urban,

municipal and regional environment. Layer 4

“Integration, Choreography and Orchestration

Capabilities” contains various services that - based

on the interoperable use of data and information from

the underlying layers - offer the possibility of

implementing new types of application scenarios.

Layers 5 and 6 contain the variety of urban and

municipal administrative processes, planning

processes and general innovations that are made

possible on the basis of the ICT infrastructure and

data from DIN OUP. The two pillars in the reference

model are responsible for data protection and security

and the general network and system management for

the integrative ICT solutions in the urban/municipal

context. They relate (vertically) to all layers and

contain capabilities for IT security and for the

efficient operation of the Open Urban Platform.

DIN SPEC 91397 (Heuser, Dickgießer et. al.,

2022): DIN SPEC 91397 builds on DIN SPEC

91357 and provides guidelines for the

implementation of digital district management

systems in the context of a Smart City/Region.

According to DIN SPEC 91377, in a modern district,

many different pieces of information/data should

flow together and be analyzed as part of an OUP

architecture and operation. By integrating various

data-generating systems, added value can be offered

in the form of new services for local residents. Such

approaches for the digitalization of neighborhoods

can provide a conceptual basis that can then be

extended and scaled to the broader smart city/region

level. The basis for digitally integrated district

management should be a secure and networked data

infrastructure that meets current demands -

particularly regarding the geopolitical situation - for

digital sovereignty.

Figure 2: The IoT Weather Station at the RheinMain

University of Applied Sciences

DIN SPEC 91377 (DIN SPEC 91377, 2025): A

few years after the provision of DIN SPEC 91357, it

was observed that a technological “patchwork” had

emerged in German and European municipalities.

This was especially shaped by the individual

solution approaches of different cities and regions in

the EU. For this reason, DIN SPEC 91377 addresses

the challenge of specifying and identifying data

models and protocols in Open Urban Platforms as a

further development of the activities in DIN SPEC

91357 and DIN SPEC 91397. This is intended to

help smaller municipalities in particular, enabling

them to communicate specific requirements to the

manufacturers and providers of ICT components for

Open Urban Platforms. In order to achieve the

aforementioned goals, work is being carried out on

the classification of existing standards, the

identification of relevant interfaces for

interoperability and abstract interfaces for the

interaction of ICT solutions within OUP instances.

Other important topics include platform security,

critical data and data governance as well as the

identification of new architectural concepts as

extensions to DIN SPEC 91357.

4 RELEVANT TECHNOLOGIES

Having described the overall standardized reference

architecture, in the following sections we point to

some key technological aspects, which are to be

considered for the envisioned energy use case

experimentation platform. Based on these

technologies, we plan to draft a simplified reference

architecture (in comparison to DIN SPEC 91357),

which is on one hand fully aligned to the principles

of an Open Urban Platform and on the other hand

capable of accommodating the needed hardware

components and software stacks.

4.1 Internet-of-Things and

Communication Protocols

IoT sensors can measure parameters such as

temperature, humidity and infrared radiation and

transmit the measured values to an IoT platform. In

order for the IoT sensors and actuators to work

together with the associated IoT platform (in the

backend/cloud), it is necessary to establish

communication channels. Typical protocols in this

domain are CoAP, MQTT, LoRaWAN, ZigBee,

IEEE 802.15.4, NB-IoT, Sigfox and 6LoWPAN.

5G is the fifth generation of standards for cellular

wireless communication, illustrated with its basic

structure and antenna examples in Figure 3. The 5G

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208

infrastructure increases data transmission rates by a

factor of 100. Network latency times improve to 1-

10 ms, while the cost of mobile data transmission is

reduced by a factor of 10 compared to 4G networks.

The development of 6G, the sixth generation of

mobile communication, has begun and will also

bring new functions for smart city solutions.

Figure 3: 5G Architectural Sketch according to (5G, 2025)

and typical gNode-B-Antennas.

Public WiFi is a service where cities offer free

wireless Internet access to their citizens including

the possibility to attach sensor and actuator devices.

Hence, WiFi also provides the city with a platform

to disseminate various information and facilitate

connectivity and relevant use cases. In the following,

we continue with showing an example of existing

IoT and communication infrastructure at our

premises.

Example of an IoT-Infrastructure at the

RheinMain university campus: A potential source

of weather data - in the form of an IoT weather

sensor station – is already installed at our university

campus and is connected to a corresponding cloud

backend. The weather station – shown in Figure 2 -

consists of two poles that are placed next to each

other and fixed to the ground using anchors. The

sensors and two metal boxes below are mounted on

one pole at a height of around 2 m, while a solar

panel is located on the other pole. A Victron Energy

Lithium SuperPack battery is located in the lower

box of the weather station. The upper box contains

the data logger and a data terminal for

communication with the Internet. The installed

router is a TP-Link model TV-MR1043ND, which

requires a power supply of 12V and 1.5A. It acts as a

WiFi bridge between the weather station and the

provided university network. An outdoor WiFi

antenna was attached to the weather station for this

purpose. The router runs under the Linux-based

open-source operating system OpenWrt.

Regarding available sensors, the weather station

utilizes various data sources such as temperature,

humidity, wind velocity and further attached sensor

devices, all of which are listed in Table 1.

Table 1: Installed Sensors and Belonging Parameters.

Measuremen

Senso

Range Precision A/D

Powe

Temperature PT-100

senso

-20℃ to

70℃

± 0,2 ℃ A 0,84W

Humidity Capacitive

senso

5% to

100%

± 1,5 % D 0,01W

Rainfall Tipping

scale

0-8

mm/min

± 2 % D 6W

Floor

temperature

PT-100 -20℃ to

70℃

± 0,2 ℃ A 0W

Leaf wet

senso

Capacitive

senso

0% to

100%

- A -

Global

radiation

Pyranome

ter

0 to

2000

W/m

- A -

Wind

velocity

ACRO-

Serial

Wind

Senso

0,3 to 75

m / s

± 2 % D 0,48W

Wind

direction

ACRO-

Serial

Wind

Senso

⸰

360

⸰

± 1 ℃ D -

4.2 Fog-Edge-Cloud Computing

Cloud computing is a paradigm that enables the

automatic provision of various computing resources

such as computing power and storage space on

demand. In cloud computing, the resources are

physically housed and maintained in large data

centers. Edge computing, on the other hand, is a

distributed computing paradigm in which

computations are performed at or closer to the data

source – the latter pertains to the specialized term

Fog Computing. Edge computing is of great

importance in the context of IoT and 5G. The

advantages of this approach are bandwidth savings,

privacy preservation (e.g. in the scope of federated

learning) and improved response times.

4.3 Data Platforms

The data platforms can encompass various types of

data, such as Open Data and big amounts of data in

general. According to the European Open Data

Portal, “open data is data that anyone can access,

use and share.” (Open Data, 2025). The main

sources of Open Data include scientific

communities, governments, and non-profit

organizations. Typical Open Data platforms include

meta-data catalogues such as CKAN and piveu as

well as semantic datastores like Virtuoso. Big data

are extremely large data sets that can be analyzed by

computers (going beyond Open Data). Big data is

A=Analog, D=Digital

Towards a Smart City Playground for Research and Experimentation of Energy Use Cases in a University Campus Setting

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the basis for a wide range of intelligent analyses,

services and applications in Smart Cities, Smart

Country and Smart Regions.

4.4 Data Analysis and AI/ML

Data analysis is the process of cleansing,

transforming, examining, and visualizing datasets,

usually with the aim of gaining insights that help in

decision-making and establishing a correlation

between the various factors involved. Artificial

intelligence (AI) and Machine Learning (ML) is an

area of computer science, in which machines react to

input from their environment by simulating human

intelligence and learning step-by-step from the

interpretation of the input values.

Typical Applications of AI in the field of IoT:

predicting maintenance work, automation failures,

connectivity problems and the intelligent

orchestration of tasks in a complex IoT system.

4.5 Dashboards and end User

Interfaces

City dashboards – as exemplified in Figure 4 - offer

the possibility to get an overall view of certain

aspects of a Smart City (area). Typically, city

dashboards aggregate data from various sources,

including city data platforms, open data portals, GIS

(Geographic Information System) systems, IoT

platforms and data from commercial data providers

(e.g. mobile network operators).

Figure 4: Example of a City Dashboard by the City of Bad

Hersfeld (Urban Cockpit, 2025).

Having outlined the key technological aspects to

consider, the following section compiles an overall

reference architecture with complete technology to

be integrated over the different layers and pillars of

an Open Urban Platform for experimentation with

energy use cases at the RheinMain UAS.

5 PUTTING THE PIECES

TOGETHER

Within this section, we put all the pieces together

and aim at defining an abstract reference architecture

aligned with the DIN SPEC 91357 Open Urban

Platform. This abstract reference architecture will

encompass the various tools and processes, in order

to enable research and teaching for IT in smart

energy use cases at the RheinMain university

campus.

The overall reference architecture is presented in

Figure 5. Starting from the bottom, one can observe

the various data sources, which will ingest input for

the data platform over the provided communication

network in the layer above. All the data is

consolidated in the logically centralized data

platform, which would be managing the various

types of data. On top of the data platform, we can

observe a layer of distributed cloud/edge services

operating on the consolidated data and implementing

different monitoring capabilities, data analytics,

dynamic energy price models and further procedures

in the scope of smart energy. This is also the layer

where AI/ML methods will be accommodated.

Finally, on top, we see the layer of end-user

applications and services that is meant to provide

data visualizations and innovative interfaces for

customers and operators. The pillars for network and

systems management, including cyber security

aspects, as well as data quality processes are

visualized on the left and right of the layered

structure in the middle. These pillars are meant to

accommodate the typical tasks of network planning,

risk/vulnerability assessment, network-/system-

monitoring and -configuration in addition to

ensuring the quality of the obtained data and the

proper functioning of the devices involved in data

acquisition (i.e. IoT sensor nodes).

Figure 5: Overall Reference Architecture derived from the

DIN SPEC 91357 Open Urban Platform.

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In the following, key technological aspects for

each layer/pillar are presented towards the

prototypical implementation for research and

teaching at the RheinMain university campus.

• Data Sources: Possible data sources at the

RheinMain university campus include our IoT

weather station, additional IoT sensors, exports

from the campus-wide energy management

system (Sauter EMS (Sauter, 2025)), data from

charging stations as well as external research

datasets.

• Communication Network: As a

communication network, we plan to utilize the

campus-wide WiFi and LAN, which would

serve as a backhaul for various IoT-gateways

(e.g. LoRaWAN, IEEE 802.15.4, SigFox …).

Furthermore, we are in the process of acquiring

an Open Source 5G implementation that we

plan to use for realizing wireless (mobile)

connectivity in a sandbox setting, in order to

connect over existing 5G and (upcoming)

beyond 5G communication standards.

• Data Platform: On this layer, we utilize a

NextCloud offer established by the local public

IT-service provider in the state of Hesse.

Furthermore, we plan to realize connectors to

service/cloud platforms such as Hadoop, HDFS

and Apache Kafka.

• Cloud/Edge Services: The cloud/edge services

will be implemented based on microservices

(e.g. docker and Kubernetes) and

API/orchestration management frameworks

like Node-RED. The microservices will be

distributed on the various available computing

resource including IoT-devices and back-end

servers. For processing the data, the standard

AI/ML and data analytics packages such as

TensorFlow, PyTorch, scikit-learn and

RDF/OWL/SPARQL with corresponding

processing frameworks, such as Jena, will be

put in place.

• End-user Interfaces: The End-user interfaces

layer will provide the possibility to experiment

with various frameworks (e.g. React, Vue.js,

responsive design ...) as well as devices such as

AR/VR-interfaces, tablets and smart phones. In

addition, information visualization dashboards

can be developed in the context of the

curriculum of the university, including the

utilization of frameworks such as D3.js and

open-source dashboard software like Grafana.

• Management: This pillar will allow us to plan

our network (e.g. 5G back-bone and

backhauling) and available resources. This can

also include network simulations such as the

OMNET++ one presented in Figure 6 and

being used in our courses on smart cities. In

addition, it is planned to establish NMS

(Network Management Systems) such as

Nagios, in order to observe the overall status of

the infrastructure and be able to take corrective

actions when required. We also envision to

experiments with aspects, such as security

monitoring, penetration testing, vulnerability,

and risk assessment, in order to train new

experts and experiment with cyber security

challenges in the scope of energy critical

infrastructure.

• Data Quality: We perceive the data quality as

a key challenge for future smart energy use

cases. This includes the data curation and

improvement for input coming from external

data sources, energy management systems and

especially from (potbe entially erratic) IoT

sensors. Hence, we plan to experimentally

realize processes (e.g. described in BPMN) for

the automated checks and verification of data

properties (e.g. format, completeness,

timeliness, and semantic checks), which have

the potential to increase the trust and quality of

the data-driven smart energy services.

Having derived and outlined the overall

reference architecture and technological aspects, the

following section continues with presenting the next

steps for research in the course of PhD projects and

advanced studies (e.g. master courses, projects and

theses).

Figure 6: An OMNET++ Simulation of a 5G Network

including Backbone.

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211

6 NEXT STEPS – A RESEARCH

AGENDA

From an information technology perspective, our

research agenda deals with four problem areas (PA):

(PA1) What do AI-supported management and

control elements look like for solving

the asynchrony problem and optimizing

the energy system?

(PA2) How must the simulation models of the

future be designed, in order to interlink

the interaction of different application,

energy storage and energy generation

contexts in terms of information

technology?

(PA3) How can the cyber security of AI-

supported energy systems be increased?

(PA4) How must the user interfaces be

designed so that they not only increase

transparency but also the acceptance of

the overall systems?

(PA5) How can we systematically ensure the

data quality required to make reliable

decisions in the scope of Energy Data

management?

To address the above formulated problem areas, we

plan to pursue the following research objectives

(RO):

(RO1) For solving asynchrony problem, (1) AI

approaches for controlling load

management must be researched that

address the predictive modeling of

power flows.

(RO2) The development and integration of a

simulation platform should make the

interaction of heterogeneous interfaces

(e.g. HVAC and PV systems) accessible

for simulations and be tested and

evaluated in apartment buildings in

particular.

(RO3) Cyber security plays a central role in the

operation and acceptance of integrated,

sustainable energy systems. With the

installation of security modules, the

resilience of the applications

implemented in the microgrid is to be

tested by penetration and security tests

and increased by appropriate solutions.

(RO4) Prototype user interfaces for price

modeling will be researched to increase

user acceptance of AI-based control of

the energy system.

The above research objectives require the following

methodological approach:

Step 1: The project pursues an iterative,

research-led development approach that

combines design science research and

agile software development.

Step 2: First, a simulation environment is

developed as an experimental field and

for the validation of AI-supported

prediction models and control

algorithms.

Step 3: To validate real use cases, an existing

Tinyhouse with an existing smart home

infrastructure that still needs to be

expanded and, if necessary, and an

energy storage system already in

operation at RheinMain University of

Applied Sciences will be used. The

Tinyhouse will initially represent a

single household, be replicated via

digital twins and integrated into a

higher-level “virtual” multi-family

house system architecture.

Step 4: In this simulation environment, modules

for energy flow visualization and

control automation can already be

developed and tested under controlled

conditions, e.g. to identify potential for

AI-based optimizations.

Step 5: The corresponding iterative

development is supported by agile

methods that ensure regular feedback

loops with users and stakeholders.

Step 6: In the second phase, the platform is

increasingly linked to real data sources.

In addition to the physical connection of

the Tinyhouse infrastructure, an existing

energy storage system is included in

order to achieve the proof of concept at

the level of a virtual multi-family house

energy management system.

SMEN 2025 - Special Session on Smart City and Smart Energy Networks

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Step 7: Finally, a prototype implementation in

the real environment of an apartment

building will be sought, provided that

suitable implementation environments

and partners for co-financing can be

found.

Step 8: On this basis, a long-term study will be

prepared to analyze the effects of the

system on energy efficiency, user

behavior and grid stability.

This methodological approach combines theoretical

modeling with practical validation, in order to

achieve robust and transferable results.

7 CONCLUSIONS

Within this paper, we presented our vision for

establishing an experimentation platform for energy

use cases at the campus of the RheinMain university

of applied sciences. We plan to follow established

Smart City standards and to create an open urban

platform integrating various data sources,

communication capabilities (5G/6G, WiFi, …) as

well as IoT devices and data platforms (e.g.

including open/big data aspects). Thereby, the idea

is to tap on existing information and datasets in

order to create an overall playground for applying

AI/ML algorithms towards the efficient analysis of

energy related use cases (e.g. asynchrony demand-

response) as well as to provide the capabilities to

train students in topics related to network

management and cybersecurity.

The envisioned playground should provide the

basis for investigating (e.g. in the course of PhD

projects) various problems areas thereby answering

clear research questions and outlining an interative

development strategy. Taking on the energy

synchronization challenge will provide the

possibility to experiment and teach various

technologies of relevance for the domain of Smart

City, Smart Country and Smart Region, which is a

key development area for the RheinMain UAS.

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