Decarbonisation of cities through data-driven intelligence
BEE Group-CIMNE
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Statistical learning methods for energy assessment in buildings with applications at different geographic levels
Presentation content
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Artificial intelligence and big data at the BEE Group
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Big data analytics for energy efficiency in buildings
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Smart grids and DR at the district level
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Energy transition and climate adaptation in communities
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Tubular biodigesters
AI & big data at the BEE Group
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Incubation of SIE
Big data analytics for energy efficiency in buildings
Big data architecture
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Big data technologies
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Linked data framework
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Ontologies
Ontologies
Linked-data
Linked data framework
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Reuse of W3C ontologies (SAREF...)
- Including additional concepts (KPIs, EEM, etc.).
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Data valorization
Harmonized energy data in our architecture:
- 5,000 buildings of Generalitat
- 400 remotely controlled buildings from infraestructures.cat
- 1,000 buildings in Bulgaria
- 377 public buildings in the Czech Republic
- 2,000 public buildings in Greece
- 4,800 CUPS of public facilities in 67 municipalities in the Province of Girona
- 70,000 monthly energy bills residential buildings BCN
Data analytics and AI
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Data analytics and AI
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BIGGR package to standardize:
- Data cleaning processes
- Abnormal periods detection
- Outliers, vacation periods...
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Data manipulation
- Interpolations
- Clusters
- Climate dependence...
- Baseline modeling
- Prediction models (day ahead)
Data analytics and AI
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What do we make with the data?
- Energy consumption prediction
- Model Predictive Control (MPC)
- PV Fault detection
- Building benchmarking
- Energy and economic impact evaluation of building Energy Efficiency Measures
Generic MPC scheme example
Smart grids and DR at the district level
DR at district level
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- User consumption modelization (aggregated)
- RLS Models with autoregresives and weather inference
- Low voltage electric grid modeling
- Electric models (detection of non-technical losses
- Optimization and DR in energy communities with centralized batteries
- Demand respond services to increase the flexibility of the low voltage grid
DR services to the Low Voltage grid
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Energy transition and climate adaptation in communities
Optimization of REC via energy allocation
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Motivation
Development of a tool to assist in the planning and operation of Renewable Energy Communities (ECs) seeking to:
1. Extract maximum potential of RES
2. Economic profitability for all participants
Optimization of REC via energy allocation
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Methodology
Optimization of REC via energy allocation
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How? GA with ordering and simplified inputs
Multi criteria optimization
Optimization of REC via energy allocation
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Case study: public buildings in BCN
128 buildings as participants; 7 solar PV installations
Comparison among 3 criteria:
1. Profitable: The selection of participants is random
The energy allocation is based on investment
1. Sustainable: The selection of participants is random
The energy allocation is based on investment
1. Optimized The selection of participants & energy
allocation is made using the optimization
Optimization of REC via energy allocation
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Conclusions
Climate vulnerability map of BCN
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Knowledge graph of the whole city
1. building level
2. census tract level
3. postal code level
Climate vulnerability map of BCN
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Heterogenous temporal graph
Train a Spatial-temporal Heterogeneous Graph Convolutional NN based on the knowledge graph
Estimate node atributes & predict new node attributes
Thanks for your attention
BEE Group
General BEE Group
By CIMNE BEE Group
