BEEMind

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En col·laboració amb:

BEEMind

Technology for resilient buildings and cities 4.0

• BEEMind is an AI powered environment that integrates three key pillars: a semantic web technologies to make data fully interoperable, a big data architecture to manage large data volumes, and a set of intelligent tools to analyze, predict, and optimize urban environments.

• It is structured around the following tools:

  • MindCity
  • MindOpera

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

MindOpera

BEEMind : MindOpera

Edificis 4.0 : Operativa global d'edificis 4.0

Modular solution for integrating operational data from public or private buildings, with the goal of improving energy efficiency, facilitating maintenance, and centralizing knowledge about their operation.

Description

Dashboard administració MindOpera

BEEMind : 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

Buildings 4.0: Global Operation of Buildings 4.0

BEEMind tools : MindOpera

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

Cities 4.0: Environmentally Smart

BEEMind tools : MindOpera

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

   • Capacity for progressive data growth and aggregated monitoring

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

4. 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

Agility and robustness in the control of tenders/contracts

Maximizes production and facilitates the justification of subsidies.

Cities 4.0: Environmentally Smart

Use cases

Value

Technology BEEMind

big data architecture

 BEEMind-ENMA

Big data integration

Ingestion

Harmonization

Ingestion

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

Integració massiva de dades

MindOpera: Data sets used

MindOpera: Data ingestion and integration

Data harmonization

BEEMind: Data ontology

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 (Ts alignment)
• Virtual meters o Kpi Calculations

BIGG: l'ontologia  de dades que enllaça des de sensors a carrers

Reuse

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

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

BEEMind: Data ontology

BIGG: the common data ontology

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

Some KPI

Infraestructures.cat

MindOpera currently supports an ecosystem of over 1,000 equipments, including:

• 1,000 with structured maintenance data,

• Over 40,000 zones and 100,000 digital assets,

• More than 1 million work orders processed.

• It integrates data from over 250,000 IoT devices via protocols such as Modbus and BACnet, collecting real-time information from building control systems.

Dashboard administració

MindOpera: Data Orchestrator

MindOpera: Data Orchestrator

Infraestructures.cat

Tecnology 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

Tool

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

Massive comparison: Energy benchmarking

Institut Català d'Energia

MindOpera: SIME

Centralized management

Energy Analytics

Energy Efficiency Measures

Institut Català d'Energia

MindOpera: SIME

Applied model intelligence (1)

1. Longitudinal benchmarking

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

Data used:

• Time series of energy consumption

• Historical climate data

• Information on intervention periods (Energy Efficiency Measures – EEMs)

Objectives:

• Compare energy performance before and after an action

• Detect changes in energy indicators of individual buildings over time

Estimation of the balance point temperature for heating and cooling periods

Detection of holiday periods

MindOpera: Data Orchestrator

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

Applied model intelligence (2)

MindOpera: Data Orchestrator

3.1 What does it do?

Evaluates the effectiveness of improvement actions and detects anomalies or deviations in real time.

• Assess the impact of retrofitting or improvement actions

• Detect anomalies or deviations in energy performance in real time

3. Action evaluation and operational diagnosis model

3.2 Data used

• Harmonized time series of energy consumption

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

• Hourly climate data

• Gas consumption

3.3 Objectives

• Validate in real time the effectiveness of implemented improvement actions

• Reduce downtime and operational costs

• Generate predictive alerts and enable intelligent maintenance

Detailed prediction of efficiency based on the relationship between energy consumption and outdoor temperature

Applied model intelligence (2)

MindOpera: Data Orchestrator

Governance and licensing

Governance and licensing

Commitment to innovation

Public investment in innovation

• Catalan

• Spanish

• European

Economy of scale

Return on investment

• High initial implementation cost

• Low maintenance cost

• Medium- and long-term return

• No monthly license fees

• Collaborative software improvement

Self-sufficiency

• Independence from big tech

• Commitment to the local tech ecosystem

• Transparency

License EUPL-1.1

An open public administration

Governance and licensing

License EUPL-1.2

Open-source license promoted by the European Commission, specifically designed for software developed by public administrations in Europe

• Legal compatibility with the European framework

• Obligation to share improvements (copyleft)

• Promotes interoperability and reuse

A solid legal framework that promotes collaboration and continuous improvement of software in the public sector

License EUPL-1.2

Thank you

Jordi Cipriano
Director of  Innovation Unit BEEGroup

cipriano@cimne.upc.edu