BEE Group solutions: BEEMind tools

MindCity

MindOpera

Platform for resilient buildings and cities

BEEMind

BEEMind is an AI powered environment that integrates three key pillars:

• Semantic web technologies to make data fully interoperable
• Big data architecture to manage large data volumes,
• A set of intelligent tools to optimize urban environments for energy transition and climate resilience

AI-powered solutions to enhance climate resilience in buildings and urban environments

Global building operations

Modular environment for integrating heterogenous data from commercial buildings and urban areas, aiming to centralize knowledge and provide intelligent solutions to building managers and urban planners

Description

Dashboard administració MindOpera

BEEMind

BEEMind

• Integration, processing, and visualization of data from cities and large building portfolios

• AI-powered models to improve climate resilience of cities and buildings and support their energy transition

• Figuring out specific high-value indicators that connect to climate action and shift to cleaner energy.

• Evaluation of energy efficiency measures and climate adaption  scenarios

Funcionalities

Environmentally Smart

• Facilitates strategic decision-making in the buildings' operation, and urban planning fields

• Optimizes resources and processes for large-scale data analysis

• Improves coordination between technical departments and end users

• Highly scalable to any city in Spain and to any portfolio of commercial buildings

Benefits

Unlocks powerful data processing solutions for buildings and cities

Platform for resilient: buildings and cities 4.0

BEEMind

• Integration of heterogeneous data (energy consumption, socio-economic, cadaster, weather, maintenance orders, energy efficiency measures, RES generation, BIM, and SCADA data...)

• Automatic harmonization of data to facilitate their interoperability (BIGG Ontology, SAREF4BUILDINGS)

• Generation of  multiple indicators at component level (self-consumption, energy indicators, climate KPIs...)

• Adaptable visualizations for each IT infrastructure

• AI and Geo AI powered modules for predictive maintenance, buildings energy optimization, urban resilience and energy transition

Functionalitalities BEEMind

One Platform two solutions

It is structured around two main frameworks:

• MindCIty
• MindOpera

Public Administration and Institutions

• Hospitals and healthcare centers.
• Universities and educational centers.
• Cultural, sports, and administrative facilities.

Targets

• Departments of the Autonomous governments with responsibilities for assets, energy, or maintenance.
• Medium and large City Councils aiming to achieve climate mitigation and adapation goals
• Consortiums, Provincial Councils, and supramunicipal bodies with centralized services.

Critical Infrastructure and Public Facilities

• Maintenance and energy services companies.
• Managers of business parks or industrial estates.
• Property administrators

Private Sector with Building Portfolios

BEEMind

Tecnology BEEMind

Ontology based big data architecture

• Orchestrates massive building operation and geospatial urban data in real time.
• AI-powered analytics, and semantic harmonization to drive energy transition, climate action and energy efficient building operation

ENMA

open-source big data architecture developed by BEE Group

G. Mor; J. Vilaplana; S. Danov; J. Cipriano; F. Solsona; D. Chemisana. EMPOWERING, a Smart Big Data Framework for Sustainable Electricity Suppliers. (2018) IEEE Access. vol. 6, pp. 71132 - 71142.

Reference paper

big data architecture

 BEEMind-ENMA

MindOpera: Data sets used

Interoperable web semantic frameworks

The ontology at the core of our solutions

Applied semantic web technologies:

Understanding and organizing data is as important as the algorithms themselves

Massive data integration

Processes:

• Renaming
• Unit conversion
• Data cleaning
• Post processing  and alignment
• Virtual meters and Key Performance Indicators (KPIs)

• Standardized schema (OWL/RDF Turtle) and shared vocabulary for describing IoT devices, buildings and urban areas, enabling interoperability.
• Supports planning, operations, and sustainability with harmonized data and KPI framework for measurement and benchmarking.

• J. M. Broto, J. Cipriano, G. Mor, O. Gavaldà, S. M.-Verki, U. Eicker (2025). An interoperable ontology-based information model for better integration of building physics and IoT data analytics models. IEEE/ACM 7th International Workshop on Software Engineering Research & Practices for the IoT (SERP4IoT). 
• E. Martínez-Sarmiento; J. M. Broto; E. Gabaldon; J. Cipriano, R. García; S. Danov  (2024).Linked Data Generation Methodology and the Geospatial Cross-Sectional Buildings Energy Benchmarking Use Case. Energies 2024, 17(12), 3006

Reference papers

Interoperable web semantic frameworks

MindOpera: Data sets used

The ontology at the core of our solutions

Understanding and organizing data is as important as the algorithms themselves

Processes:

• Renaming
• Unit conversion
• Data cleaning
• Post processing (Ts alignment)
• Virtual meters o Kpi Calculations

Reuse

• saref, s4blg, s4city, s4agri:
  • buildings, devices
• ssn:
  • systems, deployments
• geosp:
  • geolocation
• qudt:
  • units

https://github.com/BeeGroup-cimne/biggontology

Interoperable web semantic frameworks

MindCity

BEEMind tools : MindCity

                 High resilient cities in practice

This solution helps municipalities and organizations go digital and manage their data effectively on a local level.

It is a key tool for smart cities, energy planning, adapting to climate change, and making decisions about urban retrofitting

What does it do? It checks out KPIs for buildings and helps planning for energy transition and climate change adaptation:

• City microclimate model

• Rates how buildings are vulnerable to climate issues

• Forecasts energy retrofitting and adaptation strategies

• Sends heat wave communications—4 days ahead

• Supports designing urban green spaces and shelters

Description

Data integration Across Various Scales

3. Data at building level

2. Data at census code level

1. Data at postal code level

BEEMind tools : MindCity

AI powered geo models

We develop machine learning and Graph Convolutional Neural Network (GCNN) models

To enable microclimate assessment and learning patterns from geo-structured data (buildings, streets, use, energy, etc).

Estimation of KPIs for all buildings without data

Definition of future climate mitigation and adaptation scenarios

BEEMind tools : MindCity

Data sources

Massive data ingestion and integration

BEEMind tools : MindCity

BEEMind tools : MindCity

• Integration, processing, and visualization of large amounts of urban data at the building level

• AI powered models to improve cities and make them more resilient against extreme weather events.

• Figuring out specific geospatial indicators that connect to climate action and the shift towards cleaner energy.

• Evaluation of vegetation and urban climatology scenarios

Funcionalities

Cities 4.0: Environmentally Smart

• Facilitates strategic decision-making in the energy, climate, and urban planning fields

• Optimizes resources and processes for large-scale city data analysis

• Improves coordination between technical departments and citizen interaction

• Highly scalable to any city in Europe

Benefits

BEEMind tools : MindCity

Use cases

• Optimizes resources and processes
  For large-scale analysis of geo referenced urban data

• Improves coordination
  Between municipal technical departments and citizen interaction

• High scalability
  For any city in Spain

• Climate vulnerability map
  Detect vulnerable areas based on large number of KPIs

• Support buildings energy retrofitting
  Identify and prioritize buildings with the greatest needs

• Actions to adapt to heat waves
  Optimizing climate shelters, better climate communications and greening

Impacts

Cities 4.0: Environmentally Smart

Case Study: Climate Vulnerability Map of Barcelona

Vulnerability map:  Climate-Ready BCN

Source: ICLEI Action Fund 2.0
Budget: €1M
Objective: Support citizens and public authorities in adapting to extreme climate events and reducing energy poverty
Implemented: From May 2023 to December 2025

Climate Vulnerability in Barcelona at building level

The Climate Vulnerability Map of Barcelona is a geospatial analysis tool designed to identify the buildings most at risk during extreme heat events.

It evaluates key performance indicators (KPIs) for buildings and supports climate change planning

It provides an assessment of all residential buildings in Barcelona(61,000)

What is it?

CVI

How we estimate the Climate Vulnerability Index (CVI)

Vulnerability index to asses heat wave resilience

Climate vulnerability is typically framed within three key dimensions defined in the IPCC's Third Assessment Report (Intergovernmental Panel on Climate Change) in 2001:

Exposure
Sensitivity
Adaptive capacity

Although most studies classify indicators using these three categories, our index introduces additional levels to provide a more nuanced analysis while remaining aligned with the traditional framework.

Climate variability and extreme events

• Track changes over time through indicators such as temperature, precipitation, and vegetation cover. Local data helps assess risks related to heatwaves, droughts, and floods.

Energy indicators

• Explore energy use patterns (gas and electricity consumption). Indoor temperature data is crucial for identifying households at risk during extreme heat events.

Building characteristics

• Assess features such as age, use, and size to understand their impact on energy use, indoor comfort, and structural vulnerability.

Indicators groups (KPIs)

Vulnerability map

Infrastructure indicators
Assess the availability of essential public services (schools, climate shelters, social housing) and the strength of social networks to understand community support capacity.

Health indicators
Analyze the relationship between climate and health outcomes to identify vulnerable populations and prepare healthcare systems.

Demographic indicators
Not all groups are equally vulnerable. Analyzing factors such as gender, age, income, migration status, and unemployment allows for the development of more detailed CVIs.

Socioeconomic indicators
Understand urban resilience through factors such as housing costs, energy poverty, household debt, and gaps in social protection, which worsen during economic crises.

Vulnerability map

Indicators groups (KPIs)

 Climate vulnerability Index (CVI)

1. Data preprocessing – Select input data to calculate the indicators.

2. Framework selection – Determine which indicators positively or negatively affect the CVI.

3. Granularity definition – Select the spatial scale.

4. Normalization & weighting – Harmonize indicators and assign weights.

5. Aggregation – Combine indicators into groups to obtain a final value.

⚠️ Challenge: Each study adapts its CVI to its specific context and priorities.

CVI Construction

Vulnerability map

Data sources & harmonization

Main data sources

Heterogenous data ingestion

Massive data integration

Knowledge graph

Modelling

1. Overall predictive model: heterogenous temporal graph

MindCity:  Modelling

Vulnerability map

Graph Convolutional Neural Network (GCNN) models

To enable learning patterns, fill knowledge gaps and predict scenarious from geo-structured data

1. Weather downscaling microclimate model

Complementary models

Weather 

Cat boost decision tree

1.1. Heat waves prediction and communication

Complementary models

Weather 

2. Thermal demand models of buildings

Simulation of the thermal energy demand of 37,000 residential buildings.

The simulation engine is based on Reduced Order Grey Box Models (RC models) trained with TRNSYS simulations:

• Building-level predictions for space heating and cooling
• Total energy needs under 3 typologies and 17 archetypes and 5 indoor set point temperatures
• Predicting 5 retrofitting scenarios and 2 HVAC systems

Complementary models

big data IT architecture: Geospatial ENMA

Interactive visualization

MindCity:  Climate Vulnerability Map Visualization

Export and data visualization

Vulnerability map

Vulnerability map

https://maps.climatereadybcn.eu/

Index evolution by streets and buildings

Queries and forecasts

Vulnerability map

MindCity:  Climate Vulnerability Map Visualization

 Mind Opera

Operation

Description

Mind Opera is a modular solution designed for managers of public and private buildings with multiple data sources and operating systems:

harmonizes

Integrate

analyzes

Applying artificial intelligence models to optimize operations and anticipate incidents.

Consumption, maintenance, and generation data

in real time

BEEMind tools : MindOpera

Monitoring and predictive control

Energy optimization

Operation

• Modular cloud architecture

• Automatic aggregation and validation

• Multi-building supervision

• Anomaly detection

• Predictive maintenance

• Comprehensive equipment monitoring

• Semantic standardization

• Real-time intelligence

BEEMind tools : MindOpera

• Integration of heterogeneous operational data (consumption and temperature, maintenance orders, energy efficiency measures, RES generation, cadastre, BIM, and SCADA data)

• Automatic harmonization of records from multiple sources (Modbus, Bacnet, DEXMA, etc.)

• Generation of operational indicators (self-consumption, PR, CO₂ avoided, etc.)

• Adaptable visualizations for each infrastructure

• AI modules focused on predictive maintenance, control, and energy optimization

Functionalitalities

Benefits

• Orchestration and harmonization of large volumes of operational data from buildings

• Improves overall management of equipment and commercial buildings

• Reduces supervision time and generates smart alerts

• High interoperability and communication with management and maintenance systems

• Suitable for managers of public and commercial building portfolios

Operation of Buildings 4.0

BEEMind tools : MindOpera

Value proposition

Making easy energy efficiency operation....

MindOpera connects, organizes, and optimizes all your energy data on a single platform.
 

• Reduction of supervision time and human errors
• Improvement of service continuity and failure anticipation
• Optimization of energy efficiency at a portfolio scale
• Interoperable communication with existing management systems
• High scalability with the incorporation of all buildings

MindOpera

Easy

Management

Integration

Some KPI

Infraestructures.cat

MindOpera: Data Orchestrator

MindOpera: Data Orchestrator

63.258  zones

1.526

Equipments

129.205  assets

 1.302.786

workOrders

12.400 BMS device

8.526 Monitoring device

                    Environmentally Smart

Use cases

1. Centralized management of technical systems

   • Automatic generation of KPIs and operational alerts

   • Continuous monitoring of HVAC, lighting, DHW, and ventilation systems

   • Monitoring of compliance with thresholds of the different records

2. Early detection of anomalies in technical systems

   • Identification of inconsistencies in electricity generation/export or device behavior

   • Real-time performance analysis with interpolation and timestamp control

Efficient operation through reduced downtime and failure anticipation

Value

Improves service continuity and prevents penalties due to malfunctioning

Managment costs

Connection and quick action for maintenance service

BEEMind tools : MindOpera

BEEMind tools : MindOpera

3. Monitoring in multi-building or multi-company scenarios

4. Capacity for progressive data growth and aggregated monitoring

   • Performance tracing and calculation of KPIs such as PR, CO₂ avoided.
      

5. Monitoring and control of contracts or public tenders

• New control aspects of maintenance tenders: presence, energy efficiency, system availability...
• ​Monitoring of indicators created for contract management

Environmentally Smart

Use cases

Value

High scalability

• Integrate assets and sites in phases (plug-and-play with BMS/SCADA/IoT), without redoing integrations each time.

Capex Protection

• Avoids premature replacements of hardware/systems.
• Maximizes production and facilitates the justification of subsidies.

Case studies: iCAT and ICAEN

The Orchestrator of Infraestructures.cat

MindOpera: Data Orchestrator

Deploy a data orchestration infrastructure for public buildings with monitoring and remote-control systems, integrating structural, operational, and energy data.

Infraestructures.cat

Integrator of all equipment data under the direct management of Infraestructures.cat:

• Real-time data for optimal operation of hundreds of buildings

• Data provision to diferent internal and external actors. (Energy department, Maintenace department, clients, etc)

• Predictive maintenance, anomaly detection, consumption and generation forecasting

• Infraestructures.cat
• CIMNE-BEE Group

Actions

Goals

Main actors

Massive data integration

ENMA in action

Ingestion processes

BEEMind: Data ingestion

• Executed manually or periodically  
• Reading from websites, files, external databases, or APIs.  
• Implemented using Python scripts.

Dades socio-econòmiques

Dades urbanes obertes

Actualització continua dels datasets

Infraestructures.cat

Technology used

This project uses ENMA as the basic infrastructure for BEEMind:

• Kubernetes enables the management and scalability of deployed services, ensuring high availability and efficient application execution.

• Apache Kafka serves as a distributed messaging platform, enabling reliable and real-time transmission of large volumes of data between the system's components.

Direct management: Data control and analysis

The main processes are organized into four distinct stages:

     A. Data collection
     B. Data storage
     C. Static harmonization
     D. Time series processing

Model intellligence

• Predictive maintenance

• Generation forecasting

• Predictive self-consumption balancing

MindOpera: Data Orchestrator

Home

Admin Dashboard

MindOpera: Data Orchestrator

The tool

Monitoring: Tracking KPIs

MindOpera: Data Orchestrator

Control and Maintenance

Monitoring: Tracking KPIs

MindOpera: Data Orchestrator

Control and Maintenance

Monitoring: Tracking KPIs

MindOpera: Data Orchestrator

Control and Maintenance

SIME-ICAEN

Integrate and visualize the energy data of all Generalitat facilities (10,000) and support energy savings through data intelligence:

• Comparison of energy indicators

• Evaluation of the energy performance of each facility

• Verification of savings from Energy Efficiency Measures

• Planning of energy efficiency actions

Institut Català d'Energia

The project

MindOpera: Energy Monitoring System – SIME

Seguiment i avaluació del Pla d’Estalvi Energètic dels edificis de la Generalitat de Catalunya.

Goals

Equipment supervision

Monitoring and control of supplies

Monitoring and

control of certificates and audits

Global energy supervision

Monitoring of energy efficiency measures

Monitoring of projects and actions

Data provision to external services

Data verification from different sources

Data provision to external services

Massive comparison: Energy benchmarking

Institut Català d'Energia

Centralized management

MindOpera: Energy Monitoring System – SIME

Energy Analytics

Energy Efficiency Measures

Institut Català d'Energia

MindOpera: Energy Monitoring System – SIME

1. Longitudinal benchmarking

Estimation of the balance point temperature for heating and cooling periods

Detection of holiday periods

MindOpera: Energy Monitoring System – SIME

What does it do?
Analyzes a building’s energy performance over time to detect trends and assess potential energy faults.

Data used:

• Time series of energy consumption

• Historical climate data

• Calendar data

Objectives:

• Detect changes in energy indicators of individual buildings over time

2.1. What does it do?
Compares the energy performance of multiple buildings at a specific moment to identify inefficient or exemplary performance.

2.2. Data used:

• Harmonized KPIs per building

• Static data: use, surface area, climate

• Typological classification of buildings

2.3. Objectives:

• Identify best practices and critical buildings

• Prioritize actions based on comparative performance

• Generate benchmarks for new projects

2. Cross-sectional benchmarking

Identification and quantification of discrepancies between actual and historical energy consumption

Estimation of the balance temperature for heating and cooling periods

MindOpera: Energy Monitoring System – SIME

What does it do?

Evaluates the effectiveness of EEMs and energy retrofitting actions

Assessed over 400 public buildings in the Zlin Region (Czech republic) and 4,000 public buildings of Generalitat de Catalunya

Data used

• Harmonized time series of energy consumption

• Technical information from systems (SCADA, CMMS, IoT sensors)

• Hourly climate data

• Data base of applied EEMs with their application date

3.  Assessment on Energy efficiency Measures (EEMs)

MindOpera: Energy Monitoring System – SIME