- MOEEBIUS HOLISTIC ENERGY PERFORMANCE OPTIMIZATION FRAMEWORK
Energy performance optimization in buildings heavily relies on the deep and comprehensive understanding of real-life complexities imposed during actual operation. Such complexities span three interrelated groups of systems: physical systems (buildings, their equipment and their usage, along with districts and their systems), human systems (occupants and their behaviours) and the general surrounding environment (weather, its fluctuations and impact on the other systems). The inadequacy of current modelling approaches and simulation tools to effectively address these complexities is the root cause for significant prediction inaccuracies and performance deviations in actual buildings.
MOEEBIUS offers a Holistic Energy Performance Optimization Framework that treats occupants and their behaviour as the main catalyst of building sustainable operation. MOEEBIUS builds on top of dynamic modelling approaches, proven technological components and novel performance assessment and verification protocols towards enabling the alignment between predicted and actual building performance and the establishment of business friendly environments for ESCO market growth. This will be realized through the fusion of two (currently disjoint) worlds: (i) Building Information Modelling (BIM) and (ii) Occupants’ Behaviour Modelling.
Through the combination of White-Box modelling techniques (at the level of BIM and District Modelling) and Black-Box modelling approaches (focusing on occupants’ behaviour) it delivers an innovative system that captures the real complexities of actual buildings and districts and allows for the correct understanding of user behaviour’s impact. Enhanced, accurate and dynamic behavioural (individual and/ or group) profiles complement improved static BIM models (with reduced simplifications and able to accommodate LCA-LCC parameters) to enable advanced and optimized predictions through, the appropriately configured, MOEEBIUS Building Performance Simulation Engine. The holistic MOEEBIUS framework architecture is presented in the following sections.
1.1 MOEEBIUS Conceptual Architecture
MOEEBIUS adopts the Internet of Things/Services principles to establish a Holistic Energy Performance Optimization Framework applied at building and district levels. Three innovative outcomes comprise the constituents of the MOEEBIUS Holistic Energy Performance Optimization Framework:
- The Data Acquisition and Management Layer (MOEEBIUS-PIPE)
- The Building & District level Dynamic Assessment Engines (MOEEBIUS-DAE)
- The MOEEBIUS Integrated Decision Support System (MOEEBIUS-QUEST)
The conceptual architecture is presented in the following figure, followed by the detailed description of the associated architecture layers.
Figure 1. MOEEBIUS High-Level Conceptual Architecture.
The lowest level of the MOEEBIUS framework is the Data Acquisition and Management Layer (MOEEBIUS-PIPE) which is responsible for the collection of all necessary information, through Building Energy Management Systems (BEMS), District Heating Management Systems (DHMS), sensors deployed for monitoring ambient and hygienic conditions, energy meters and external sources (weather, pricing). Through open and semantically enhanced middleware infrastructures (applied both at the building and the district level) standardised multi-directional communication and control interfaces will be established between the individual building components and district-wide systems and the different MOEEBIUS modules and sub-systems. The middleware establishes a seamless, transparent and homogeneous interface to all sensor/ actuator/ metering and external components.
The MOEEBIUS performance optimization mechanisms will be based on an enhanced BEPS which comprises an extended version of available open-source tool like EnergyPlus. The MOEEBIUS BEPS accommodates enhanced algorithmic concepts for bringing together improved BIM models, semantically enhanced Distributed Energy Resources (DER) models and dynamically updated occupants’ behaviour profiles, schedules and weather forecasts and utilizing them in building performance simulation iterations towards offering optimized performance predictions.
More specifically, the MOEEBIUS Occupant Profiling Mechanism (OPM) properly manages and trains the enhanced integrated comfort models introduced in MOEEBIUS by utilizing information streams from WSN. The profiling mechanism utilizes real-time energy data and ambient information in order to define dynamic consumer flexibility profiles and enable energy performance optimization through automated control strategies that balance energy performance with comfort and indoor quality requirements.
The MOEEBIUS Dynamic Assessment Engine (DAE) comprises a main innovation introduced in MOEEBIUS and is based on a Distributed Fuzzy Model Predictive Control (DFMPC) scheme. A method for control of large-scale multi-rate systems is adopted including fast and slow dynamics. These systems are multi-rate in the sense that either output measurements or input updates are not available at certain sampling times. The multi-rate nature gives rise to lack of information which causes uncertainty in the system’s performance. To compensate the information loss due to multi-rate nature of the system a distributed Kalman filter  is applied to provide optimal estimation of the missing information. The distributed nature of the DAE enables implementation in a scalable way, setting an easily exportable structure at district level.
Figure 2. The MOEEBIUS Dynamic Assessment Engine – Conceptual Design.
Finally, the End-User Application Layer (MOEEBIUS-QUEST) accommodates the integrated MOEEBIUS Decision Support System (DSS) and respective applications for predictive/ sanitary maintenance, retrofitting strategies, evaluation and demand flexibility analysis, aggregation and forecasting, comprising the front-end for the end-users and enabling optimal decision making (not only in terms of near future actions for maintenance and retrofitting, but also in real-time, enabling fine-grained control for holistic energy performance optimization), through the integrated MOEEBIUS DSS.
Figure 3. Conceptual Representation of dynamic real-time optimization features of MOEEBIUS.
MOEEBIUS-QUEST consists of the business layer of the MOEEBIUS framework, providing the tools for the different business stakeholders: ESCOs, Facility Managers & DSM Aggregators.
To find more information on the MOEEBIUS Holistic Energy Performance Optimization Framework, please visit the following website: http://www.moeebius.eu/library/reports