An Integrated Building Management Platform for Investment into

Renewable Energy System and SRI Compliance

Giuseppe Rocco Rana

, Giuseppe Mastandrea

, Marco Antonio Insabato

Reshma Penjerla

and Luigi D’Oriano

Energy@Work, Bari, Italy

Keywords: Smart Readiness Indicator, Renewable Energy Systems, Building Management System (BMS) Integration,

Optimization Algorithms, Energy Efficiency, Investment Advisor, Energy Simulation,

Decision-Making Tools, Sustainable Buildings.

Abstract: The goals of ecological transition in habitations require an increasing number of considerations to ensure that

newly installed systems or building management solutions are economically advantageous and effective in

terms of energy savings and production. The increasing variety and supply of renewable energy systems, and

the increasing demand for them require tools that meet the needs of building stakeholders (e.g., building

owners and facility managers) to ease the transition as well as provide consistent metrics to measure the

validity and integrated simulation to facilitate investment decisions and track ecological transition progress

over time. This paper introduces a comprehensive toolset with multiple features, including the simulation and

management of Renewable Energy Systems (RES), the Building Management System (BMS) integration, and

the calculation and simulation of the Smart Readiness Indicator (SRI). This toolset collectively assesses the

readiness of a building toward an ecological transition. Specifically, the system includes: (1) an Advanced

SRI Calculation Engine, which implements both simplified (Method A) and detailed (Method B) SRI

calculations for various European regions providing precise evaluations of smart building capabilities across

domains such as heating, cooling, ventilation, lighting, and energy monitoring; (2) a continuous tracking of

building’s smart readiness evolution enabled by seamless BMS Integration that allows real-time monitoring

of building systems and allows a continuous tracking of a building's smart readiness evolution; and finally (3)

an Optimized Investment Advisor which offers tailored recommendations for investments in smart building

upgrades, renewable energy installations, and energy storage systems, employing advanced optimization

algorithms to ensure cost-effectiveness and energy efficiency. Developed as part of the INSPIRE, an

experiment under the SUSTAIN EU project Open Call for smart building innovations, this toolset aims to

enhance decision-making processes, improve resource allocation, and foster a holistic approach to achieve

smart, sustainable, and energy-efficient buildings.

1 INTRODUCTION

The INSPIRE (INteroperable open and modular

energy management System with integrated

Performance Improvement and optimization SRI

calculation and support for energy and REnewable

investments) endeavors to establish a modular,

interoperable energy management system that

incorporates an advanced Smart Readiness Indicator

(SRI) calculator and supports investments in

renewable energy sources. This initiative is focused

on enabling building users to achieve independence

from fossil fuels by using the SRI (SRI

Implementation Tools, s.d.) to assess and enhance

https://orcid.org/0000-0002-3353-3239

https://orcid.org/0000-0002-1579-8030

https://orcid.org/0000-0002-6490-5277

https://orcid.org/0009-0008-9868-5955

https://orcid.org/0000-0003-0208-5776

346

Rana, G. R., Mastandrea, G., Insabato, M. A., Penjerla, R. and D’Oriano, L.

An Integrated Building Management Platform for Investment into Renewable Energy System and SRI Compliance.

DOI: 10.5220/0013379000003896

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

In Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering (MODELSWARD 2025), pages 346-353

ISBN: 978-989-758-729-0; ISSN: 2184-4348

Figure 1: Conceptual architecture of the INSPIRE platform.

building capabilities, thereby facilitating investments

in renewable energy systems (RES) and smart

technologies integrated with advanced IT systems.

The system comprises three primary software

components. The first, an Advanced SRI Calculation

Engine, offers a user-friendly platform for deep

evaluation of a building’s readiness for smart

technologies. The second component, the Seamless

BMS Integrator, leverages the BEMServer open-

source solution to enable real-time insights and

operational optimization, representing a significant

advancement from static to dynamic SRI assessments

through responsive building management. Lastly, the

Optimized Investment Advisor component evaluates

installations of RES (Verda et al., 2022) and smart

devices to develop balanced investment plans that

emphasize energy efficiency and reduce

environmental impact. Collectively, these

components illustrate the INSPIRE integrated

approach to promoting sustainable and intelligent

building environments.

2 LITERATURE REVIEW

The development of tools to assess a building's

readiness for ecological transition aligns with the

European Smart Readiness Indicator (SRI)

framework, which emphasizes energy efficiency,

smart technology integration, and user-centric

adaptability. Several recent studies have highlighted

the strengths and limitations of the SRI methodology

across diverse contexts. For instance,

(Apostolopoulos et al., 2022) explored retrofitting

scenarios to enhance smart readiness in various

building typologies, demonstrating cost-effective

pathways to improve SRI scores but revealing

inconsistencies in service applicability and subjective

assessment criteria. Similarly, (Papadopoulos et al.,

2024) proposed simplified financial indicators to

bridge technical and economic considerations,

promoting accessibility and adoption among diverse

stakeholders. Comparative analyses, such as those

by(Samaras et al., 2024), underscore the regional

variations in SRI adoption readiness, highlighting

gaps in policy frameworks and technological

infrastructure across EU countries. These studies

collectively underscore the need for a comprehensive,

adaptable assessment toolkit that integrates SRI

frameworks with real-time data, financial metrics,

and multi-criteria decision-making models to ensure

broader applicability and alignment with ecological

goals. The tools presented in this paper contribute to

this evolving landscape by offering enhanced

methodologies and comparative insights, addressing

previously noted limitations in SRI's adaptability and

practical implementation.

3 SOFTWARE ARCHITECTURE

The Software Architecture is meticulously designed

to consolidate various components to augment

building intelligence, energy efficiency, and

investment optimization through streamlined front-

end and back-end interactions.

The architecture comprises a number of

components, organised as follows:

An Integrated Building Management Platform for Investment into Renewable Energy System and SRI Compliance

347

Dashboard for Data Visualization: This

dashboard employs HTML, CSS, and JavaScript to

craft a user-friendly interface that supports data

analysis, Smart Readiness Indicator (SRI)

calculations, and visualizations. It incorporates tools

such as OpenLayers for map integration, Ol-

Geocoder for geocoding services, and DataTables for

managing lists of buildings. The GUI enables

building managers to effectively visualize data and

interface with the Building Management System

(BMS).

Back-end Platform: The back-end platform is

engineered to facilitate critical functionalities

including the Advanced SRI Calculation Engine,

Optimized Investment Advisor, and Seamless BMS

Integrator. This platform is integral in performing

calculations that optimize investments and enhance

building intelligence and energy production

capacity. It integrates external data and utilizes APIs

for real-time monitoring and data acquisition from

IoT devices. The back-end supports the front-end

dashboard through sophisticated data processing

and decision-making tools, aimed at maximizing

energy efficiency and optimizing smart building

operations.

3.1 SRI Calculator

The Smart Readiness Indicator (SRI) is a European

Commission initiative under the Energy Performance

of Buildings Directive, designed to measure a

building’s capacity to utilize smart technologies that

facilitate decarbonization and enhance living comfort

and efficiency.

It evaluates a building's 'smartness' based on its

ability to sense, interpret, communicate, and actively

respond to the dynamics of technical systems,

external environmental factors (including energy

grids), and occupant needs.

The methodology for calculating the SRI is based

on the multi-criteria assessment method defined in

Commission Delegated Regulation (EU) 2020/21551

(Delegated Regulation - 2020/2155 - EN - EUR-Lex,

s.d.) and provide two main Methods, the simplified

one (Method A) which is based on a limited,

simplified catalogue of 27 services and the Method B,

based on lists full catalogue of 54 services.

The formula for calculating the SRI can generally

be simplified as follows:

𝑆𝑅𝐼 =

∑

𝑊

,

⋅



𝑆

,

∑

𝑊

,

∗ 100

(1)

Figure 2: User-Friendly Data Input Interface for the SRI

Calculator.

Where:

• 𝑊

,

: Weight assigned to service i under the

country-specific framework.

• 𝑆

,

: Score of service i under the country-

specific framework, which may include

adjustments for local definitions or

thresholds.

•

∑

𝑊

,

: Total weight of all services after

country-specific adjustments.

• The result is multiplied by 100 to express the

SRI as a percentage.

This formula encapsulates the weighted average

score of services adjusted for local conditions,

expressed as a percentage.

A detailed SRI calculation tool using Method B

has been implemented across all regions. The tool has

developed and integrated essential components for

calculating total SRI scores, impact scores, domain

scores, detailed (partial) scores, and aggregated

scores. All data are stored in a PostgreSQL database,

managed through the Flask framework, with HTTP

REST APIs enabling interactions with the Optimized

Investment Advisor tool.

3.2 SRI Optimisation Component

The Optimized Investment Advisor tool delivers

investment recommendations by leveraging SRI

MODELSWARD 2025 - 13th International Conference on Model-Based Software and Systems Engineering

348

scores, budget limitations, and specific device

catalogues relevant to Italy, the Netherlands, and

Cyprus. It employs sophisticated optimization

algorithms, such as the Knapsack Branch and Bound

method (Bednarczuk et al., 2018), to propose optimal

investment strategies that improve building

intelligence. These recommendations are seamlessly

integrated with the comprehensive SRI score

obtained from the SRI Calculation Engine.

Figure 3: User-Friendly Interface of SRI Calculator Results.

Below is a detailed description of the

mathematical structure used for the calculations on

the backend, which outlines how the optimization

process is approached.

Objective:

Maximize the total “profit” (Total SRI Score):

Maximize ∑

i=1

(2)

Constraints:

The total “weight” (cost) of the selected items (smart

devices) must not exceed the capacity (Budget):

∀𝐶 ∑

i=1

i ≤

W (3)

Variables:

n: Number of items (smart devices).

: Weight (cost) of item

: Profit (total SRI score) of item

: Binary decision variable indicating whether item

i is selected (1) or not (0).

W: Maximum capacity (budget).

𝐶: Cost type (Main Automation Cost, Installation

Cost, Operational and Management Cost).

3.3 RES Optimiser

The RES optimizer provides the possibility to

determine the best set of renewable energy systems,

in order to maximise renewable energy production

and incentivise self-consumption as well as

production, in order to reduce reliance on the grid,

given the market costs per kWh. The optimisation

consists of minimising the LCOE using Constrained

Integer Linear Programming techniques (Omu et al.,

2013) as well as a Constrained Search Problems

(Hannan et al., 2020), (Khan et al., 2020), (Yang et

al., 2022). A number of devices are available as

possible solutions for the energy optimiser, said

options being photovoltaic panels, wind turbines,

energy storage battery solutions, and micro co-

generation plants to replace the older, less efficient

boilers.

3.3.1 Building Selector

The building selector consists of providing an

interface in order to find the building of interest on

which to perform the analysis and optimisation of the

potential Renewable Energy Systems to apply. The

building selection then saves the building according

to its coordinates, its local total irradiance value, and

thus the potential performance with solar energy

system, as well as the degree days in order to assess

the potential heating energy required to activate the

systems. Last but not least, the intuitive shape

selection allows users to delineate the area of the

building that will be used for their optimizations.

3.3.2 Optimization

The optimisation procedure allows users to select

their preferred RES (Renewable energy systems) as

well as provide further information about the building

layout, about whether the building has a sloped or flat

roof, the height of the available façade as well as roof

availability percentage.

Next, there is the selection of potential RES to

include in the optimisation. These are in turn divided

into:

• Photovoltaics (PVs): based on the irradiance

values obtained from PVgis (PVGIS data

sources & calculation methods, s.d.) and

considerations about PV panel degradation over

ten years of use, reflecting the typical decline in

energy production efficiency as PV panels age,

the total power per PV is estimated and used to

determine energy production

• Vertical Axis Wind Turbines (VAWT): the

reason for VAWTs is their smaller form factor

compared to the industrial case, allowing for

decentralised wind energy production.

Determining their energy is dependent on the

maximisation of the energy produced by the

rotor according to the Betz principle (van Kuik,

2007) and then the obtained energy production

is used in the main optimisation function

An Integrated Building Management Platform for Investment into Renewable Energy System and SRI Compliance

349

• Micro Combined Heat and Power (μCHP):

through the modelling of the internal building

spaces, as well as the energy class in accordance

to European and Italian standard as well as

degree days estimation, it is possible to obtain a

linear model of the shape factor and estimate the

produced thermal energy and thus the recovered

electrical energy of a μCHP within the

optimisation

• Energy Storage: It is considered according to

the type of profile the building is assigned to

(office, apartment, warehouse) in order to

determine the self-consumption and thus the

sizing of the battery to maximise storage or

minimise battery draw.

Finally, the system is optimised in accordance to

the following objective function:

𝐿𝐶𝑂𝐸 =

∑

𝐶



⋅

∈

𝑛



∑

𝐺



⋅

∈

𝑛



(4)

Where:

𝐶



: cost per unit of the individual RES

𝐺



: revenue per unit fo the individual RES

LCOE standing for Levelized Cost of Energy.

The feature of this function is being able to balance

supply and demand of energy, to minimise costs and

maximise self-consumption, thus reducing energy

draw from the grid. Data about the building such as

roof size, internal space, façade width and such

determine spatial constraints and energy needs.

3.4 Building Management System

Integration

In order to provide a complete solution, an existing

building management has been incorporated through

its core component and an API: Bemserver (Bourreau

et al., 2019). BEMserver is an open source, AGPL-

3.0 licensed, building management system with built

in functionalities to manage buildings, districts, and

apartments, as well as datetime data. This datetime

data enables real-time insights and enhanced analysis,

particularly in evaluating the impact of

recommendations provided by tools such as

acquisition campaigns. These campaigns are

designed to monitor and assess the system’s

performance, allowing building managers to verify

improvements in self-consumption or enhancements

in the Smart Readiness Indicator (SRI) metric. This,

in turn, boosts the building’s interconnectivity and

overall efficiency.

4 TESTS AND RESULTS

4.1 SRI Calculator Validation

The analysis in the table below showcases the

assessment of differences between SRI scores derived

from the EU Excel sheet for SRI calculation (SRI

Package v4.5) and those calculated using the

INSPIRE tool for buildings in Italy, across multiple

levels of the different domains. Each domain's

functionalities were tested independently to ensure

accuracy, maintaining a 100% efficiency baseline for

all her domains. The results for each level were then

aggregated and averaged to determine the overall

variance within the domain. This structured approach

allows for a precise evaluation of the SRI tool's

performance in comparison to the baseline data. The

table also includes a breakdown of average

percentage differences across various building

domains, highlighting the minor discrepancies

observed and providing a clear overview of the tool’s

consistency and reliability in SRI score calculations.

Table 1: Comparison of SRI Scores Across Europe: EU

Excel for SRI calculation vs. INSPIRE Tool.

Average value of the Differences (In Percentage) for all the Regions

Domains West

Europe

South

Europe

North

Europe

South

East

Europe

North

East

Europe

Heating 0.08% 0.02% 0.02% 0.03% 0.09%

Cooling 0.09% 0.03% 0.02% 0.11% 0.01%

Ventilation 0.02% 0.01% 0.05% 0.01% 0.01%

Domestic

Hot Water

0.07% 0.06% 0.10% 0.03% 0.08%

Lighting 0.01% 00% 0.01% 0.0% 0.0%

Dynamic

Building

Envelope

0.0% 0.0% 0.01% 0.0% 0.0%

Electricity 0.08% 0.02% 0.04% 0.06% 0.10%

Electric

Vehicle

Charging

0.14% 0.14% 0.08% 0.14% 0.14%

Monitoring

and control

0.06% 0.03% 0.06% 0.04% 0.01%

4.2 SRI Optimizer Validation

The SRI Optimizer has been tested and the tests

implemented have confirmed that the knapsack

algorithm provides accurate and optimal solutions for

the given inputs, maximizing the Smart Readiness

Indicator (SRI) scores within specified budget

constraints, by correcting the minor issues in the

MODELSWARD 2025 - 13th International Conference on Model-Based Software and Systems Engineering

350

functions of the optimizer and proving its effectiveness

in decision support for smart device investments.

Table 2: Comparison of different tests implemented to

validate the SRI Optimizer.

Country

Avg. Invest.

costs

Avg.

Initial SRI

Avg.

Final

SRI

Avg. Final

investment

% SRI

error

5000

20 23 4988,1 0,15

50 55 4993,16 0,06

10000

20 26 9971,65 0,79

50 59 9911,57 1,59

50000

20 57 48432,3 5,72

50 98 49086,7 2,17

100000

20 100 92240,6 15,52

50 100 99378,5 6,21

5000

20 23 4991,69 0,21

50 55 4976,42 0,12

10000

20 28 9956,66 7,19

50 59 9955,73 3,22

50000

20 47 49454,87 14,45

50 100 48756,88 24,86

100000

20 100 94501,81 21,99

50 100 98651,08 26,98

5000

20 24 4986,55 0,25

50 54 4999,33 0,01

10000

20 27 9975,58 0,89

50 58 9968,23 0,50

50000

20 59 49578,4 10,14

50 83 49708,34 4,81

100000

20 100 99847,47 12,20

50 100 97842,96 5,39

To facilitate enhanced Smart Readiness Indicator

(SRI) scores and elevate the building's smart

capabilities, users are prompted to select their country

of interest currently available options include Italy, the

Netherlands, and Cyprus. Additionally, users must

specify their preferred cost type, choosing from Main

Automation Cost, Installation Cost, or Operational

Management Cost, along with their budget constraints.

Utilizing the Knapsack Branch and Bound algorithm,

coupled with a comprehensive device catalogue, the

system strategically optimizes SRI scores by

minimizing costs within the defined budgetary limits.

The optimization outcomes are presented in terms of

total equipment purchase cost and optimized SRI

scores, accompanied by recommended investments.

This allows users to meticulously review and consider

suggestions for further enhancements, thereby

fostering informed decision-making to improve the

building’s smartness effectively.

The Table 2 presents the results of tests conducted

for Italy (IT), the Netherlands (NL), and Cyprus (CY)

to evaluate the performance of the SRI optimizer

across different investment sizes (€5,000, €10,000,

€50,000, and €100,000) and initial SRI values (20 and

50). For each scenario, the average final SRI and

average final investment calculated by the optimizer

are reported, alongside the percentage error in SRI

compared to the traditional calculation method using

the EC spreadsheet. The data show that the SRI error

generally increases with larger investments and

higher SRI targets, with significant discrepancies

observed particularly in the Netherlands at higher

investment levels. This highlights the varying

accuracy of the optimizer across different contexts

and its performance relative to established methods.

4.3 RES Optimiser Validation

The RES Optimiser has been validated in accordance

to a test set consisting of a variety of conditions in

terms of location within Italy, as the Degree Day data

was the most available, as well as provide diverse

conditions in terms of climate due to the length of the

country.

Outside of that, a number of conditions have been

tested, namely:

Table 3: Input variables.

Parameter Name Value set

Building profile Medium size apartment

Medium office

Warehouse

Interior space 220 m

440 m

1760 m

Energy consumption per

square meter

80 €/m

120 €/m

160 €/m

Energy classes D

BIPV Installation Not expected

Installed on roof

Installed on wall

μCHP Not expected

Expected

Location

(latitude, longitude)

40.74805 17.38009

41.87028 13.12521

45.58191 12.76365

40.304750 18.222830

An Integrated Building Management Platform for Investment into Renewable Energy System and SRI Compliance

351

As a result, the following algorithms were compared:

 COBYLA: gradient free optimization

algorithm (Powell, 1994)

 SLSQP: Sequential Least Squares Quadratic

programming (Joshy & Hwang, 2024)

 SHGO: simplicial homology global

optimization(Endres et al., 2018)

 CP-SAT: Constraint satisfiability

programming(Python Reference, s.d.)

 TRUST CONSTRAINT: global search

problem for inequality Constraints (Yuan,

2015)

The results are as follows, on average

Table 4: Algorithm results in terms of success rate, average

iteration number and objective function output.

Algorithm

Success

Rate

Iterations LCOE

COBYLA 95.88% 766 0.59

SLSQP 33.33% 3740 0.55

SHGO 100% 250 0.61

CP-SAT 100% N.A. 0.92

TRUST

CONSTRA

INT

24.28% 898 0.77

Table 5: performance of the algorithms in terms of roof

usage, investment usage and energy savings.

Algorithm

roof

occup. %

Invest.

usage %

energy

savings %

COBYLA 25.15 79.86 48.9

SLSQP 17.41 56.2 33.52

SHGO 27.98 79.75 48.93

CP-SAT 49.57 74.38 25.25

TRUST

CONSTr.

38.93 111.25 48.71

4.4 BMS Communication Validation

The BMS API has been validated in terms of

integration for the inclusion of data. A number of tests

have been performed using synthetic data, where a

sample building has been created, being in a specific

district, with a number of apartments, all being

included in an acquisition campaign. Being integrated

in the main website for data acquisition. Said data has

been used in order to test the integration level of the

BMS with the rest of the tools.

5 CONCLUSIONS

This paper highlights the successful development and

implementation of the INSPIRE project toolset. The

integration of the Advanced SRI Calculation Engine

and the Optimized Investment Advisor has

demonstrated its potential to enhance decision-

making processes, optimize investments, and provide

insights into a building's transition. Furthermore, the

inclusion of the Continuous Tracking System enabled

by a seamless BMS integration has opened a dynamic

assessment and tracking of a building's smart

readiness evolution. These insights can contribute to

proposing a refinement of existing SRI frameworks,

bridging the gap between static evaluations (Methods

A and B) and a more dynamic, performance-based

approach envisioned for future Method C by the EU

Commission. Future developments will be addressed

to enhance optimization algorithms and broaden

system integrations to support diverse SRI-based

business models. Finally, these tools have created a

holistic framework that empowers stakeholders to

achieve measurable progress in the ecological

transition of buildings: by offering robust decision-

making tools and scalable solutions, the toolset can

facilitate sustainable building practices across various

contexts.

ACKNOWLEDGEMENTS

This research was part of the INSPIRE experiment,

funded by the Open Call of the SUSTAIN project

(Grant Agreement No 101074311) and also builds

upon the results of activities conducted by

Energy@Work as a partner in the EasySRI project

(Grant Agreement No. 101077169).

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