Published on March 14, 2014
Energy Management of Buildings & Renewable Energy Systems using BIM Nick Tune Director – BRE, BuildingSMART UK & International
The Building in its Environment Model of the Interaction of the Building with the Environment and Users Internal Processes e.g. Occupancy Context Climate Geology Urban Context Dynamic (Real-time) Self-organizing (Self-updating)
Energy Performance Gaps: The Need for a ‘user in the loop’ intelligent Solution PREDICTED ENERGY PERFORMANCE ACTUAL ENERGY PERFORMANCE INTRINSIC ENERGY PERFORMANCE DESIGN PHASE CONSTRUCTION PHASE OCCUPANCY& OPERATIONSPHASE Energy consumption Performancegap PRE- DESIGN PHASE EXPECTED ENERGY PERFORMANCE The Challenge
The Building Energy Management Hierarchy• Level 1 Take Action based on monitored results . These systems, nowadays, are also used to improve energy performance of buildings and not just for comfort or security reasons. • Level 2 Most recent technological developments based on artificial intelligence techniques such as neural networks, and genetic algorithms. (the current state of the art) ? LEVEL 2 : Neural Netowrk/Optimisation/ Simulation Models LEVEL 1 : Building automation systems (BAS)
Level 2 - EU FP7 project - SPORTE2 • Intelligent management system to integrate and control energy generation, consumption, and exchange for European Sport and Recreation Buildings. • Real time energy management. • BRE Trust centre at Cardiff university involved in optimisation module development.
• Scenario: Optimisation of the Air handling units in swimming pool zone. • Control parameters: Supplied air flow rate ; supplied air temperature. • Objective : Minimise energy consumption ; maintain Comfort Remote Energy Optimization in Sport Facilities
Remote Energy Optimization in Sport Facilities Sensors relay information Optimum parameters to be controlled by pilot initial optimum Supplied Air flow rate (m3/s) 1.6 2.391 Air temp. Inlet (deg. C) 4.827 9.279 obj 5.503 0.105 Elec_Cons (kwh) 0.036 0.039 Therm_Eng_Cons (kwh) 0.354 0.025 PMV 5.113 0.042 Stage 1 Artificial Neural Network models Stage 2 Optimisation
EU FP7 project - SPORTE2
Why Level 3 ? • Scenarios do not consider a holistic viewpoint. • Solely numerical optimisation. • The results shown above …. Can we achieve this in reality ?
Level of Building Energy Management solutions 10 Size of market Cost to deploy £ £ Savings kWh
Ontology • Ontology – a data model that represents knowledge as a set of concepts within a domain and the relationships between these concepts. • Form of knowledge management. (Marco Grassi, 2013)
Workflow Building Automation systems Optimisation ONTOLOGY
Level 3 - Ontology at a Building Level • Ontologies allow us to see the bigger picture - whole building context for more efficient energy management in buildings. • Add layer of intelligence into traditional optimisation process through human expertise , or simulation models or derived from historical data
• We adopt a semantic approach to energy optimisation • Enhanced version of BIM is augmented with energy saving rules • Real time account of the buildings performance is interpreted via the BIM to suggest energy optimisation strategies • BIM is no longer static model conveying info it becomes a dynamic knowledge base that informs FM to make energy optimisation decisions Approach to realise energy savings 22. November 2012, 1. Review Meeting in Brussels 14
Energy Model Scenario Definition Sensitivity Analysis Simulation -based Rules Real World Sensors Historical Data Mining Database Predictive Rules Dynamic Ontology Real Time Control and Actuation Historical Data approach: • More accurate representation of a building through its metered data • Restricted by monitored data • Lack of understanding of the building energy phenomena Simulation approach: • Holistic coverage of the building energy equation • Acute understanding of governing variables and parameters • Energy baseline data Two Complementary Approaches to Energy Saving
Fuzzy RT Reasoning Module Mapper SPARQL Endpoint Engine RDF Triple/Quad Store Results (RDF/XML, N-Triples) Rule / Inference Engine over SWRL Rules Jess Fuseki Mining Database OWL Knowledge Base + SWRL Rules (Rule Base) SWRL Rule Engine Bridge KnoholEM - Architecture Data Mining Engine Learning from data (ANN) Rules Specific KB (building) Building Readings from sensors and set-points RDBMS RTController Knowledge New Facts and (DL Safe) Rules Historical Data updated with frequency: 5, 10 or 15 minutes Optimization results benchmarking SPARQL Queries (over HTTP/ SOAP) GUI Action Suggestions Querying for EE energy anomalies and new instances (building behavior) (SPARQL) Demo Objects Energy Model Variable Oriented Energy Simulation Data Sets Rule calibration Process Energy simulation generated datasets from identified sensitive variables possible range values New set-points to BMS Controls BMS Control Interface Current Rules Semi- automatic Process Metering Controller retrieves RT Data (last updates) BMS Aligned Data
Occupant Interface – Activity Modeller
FM Interface – Monitoring and Control
• Forum – Extra Care unit Holland • University – The Hague, Holland • Blue Net office, Seville • Ideas office, Seville • Media TIC office Barcelona Buildings being trialled 22. November 2012, 1. Review Meeting in Brussels 19
Level 3 - Ontology at a DISTRICT LEVEL Energy Optimization at District Level Energy Storage Large Scale Renewable Energy Generation
District Energy System Real-time management framework Purpose-built ontology and international standards Ontology management system District editor District simulation framework District Energy Visualisation Tool Multiagent-based coordination OWL/RDF back-office Renewable Energy Management Approach
22 Little White Alice, Cornwall Taken learning from previous projects to deliver a solution to small estates of buildings – Energy Minimisation and Renewable Energy Optimisation
Thank you for listening 23
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