Multiscale building modelling and energy simulation support tools
Building and district modelling (BIM, CityGML. are key technologies for the deployment of energy efficiency strategies at building and district level, from the initial stages of planning and design to the operation and maintenance ones. These technologies allow satisfying the interoperability requirements that facilitate the cooperation among the multiple stakeholders and provide the framework to develop more intelligent tools. This paper introduces five complementary European R&D projects in which TECNALIA is collaborating, very good examples of innovative systems based on these concepts. MOEEBIUS enhances passive and active building elements modelling approaches enabling improved building energy performance simulations. HOLISTEEC focuses on building multi physical simulations considering the neighborhood context. FASUDIR exploits the high potential of GIS tools for urban sustainability analysis and accurate building energy performance evaluation. EFFESUS integrates district and building scales in historic districts. OPTEEMAL develops a platform at district level, based on an IPD approach. al/measured energy performance of buildings to values below 10% by addressing occupants' behaviour, real HVAC (Heating, Ventilation and Air Conditioning) performance and real weather conditions both at the building energy performance simulation (commissioning) as well as during the operation phase (real time optimization on the basis of fine grained control and automation) MOEEBIUS solutions will be validated in reallife conditions over an extensive 20 month pilot rollout period (that includes equipment and systems installations, base lining activities, models and systems calibration and actual validation of MOEEBIUS in real life situations) in a variety of buildings (office, retail, educational, sports, residential, hotel) and building blocks (considering their interactions in energy performance optimization) under different environmental, social and cultural contexts in three dispersed geographical areas (London in UK, Mafra in Portugal and Belgrade in Serbia) 2.1 Modelling approach MOEEBIUS introduces a holistic modelling approach that focuses on appropriately addressing and accurately understanding all sources of uncertainty and inaccuracy in building performance assessment. MOEEBIUS adopts a hybrid approach that combines white box modelling techniques (at the level of BIM) with black box modelling approaches (focusing on occupants' behaviour) to deliver an innovative system that captures the real complexities of actual buildings 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 life cycle assessment and life cycle cost parameters to enable advanced and optimized predictions through, the appropriately configured, MOEEBIUS building energy performance simulation engine. 2.2 Simulation approach MOEEBIUS uses a two step calibration process of dynamic simulation tools that considers as built characteristics, set points, local real weather data, real occupancy (first step) and sub metering HVAC/lighting data, real indoor temperature (second step) The MOEEBIUS performance optimization mechanisms are based on an enhanced version of the already available open source and widely used EnergyPlus simulation engine. It accommodates enhanced algorithmic concepts for bringing together improved BIM models, semantically improved with DER (Distributed Energy Resources) models and dynamically updated with occupant behaviour profiles, schedules and weather forecasts and utilizing them in building performance simulation iterations towards offering optimized performance predictions of high accuracy. Even though modelling comprises a focal point of MOEEBIUS, the core outcome and main innovation introduced in the project lies upon the MOEEBIUS dynamic assessment engine. At building level, the dynamic assessment engine serves two distinct, but also interrelated functions: (i) fault detection and diagnosis and (ii) distributed fuzzy model predictive control. Simulation outputs and real time measurements from BEMS (Building Energy Management Systems) the MOEEBIUS wireless sensor network and external sources (weather data) are fed to the dynamic assessment engine and comparatively assessed for the identification of performance deviations and their root causes. Through fault detection and diagnosis, the dynamic assessment engine is able to recognize whether the building is beginning to operate sub optimally; and proactively identify specific performance trends at different spatio temporal granularity (e.g. abnormal HVAC consumption increase in a specific room) that could progressively lead to significant performance deviations. Subsequently, it is able to drill in and analyze parameters affecting the deviating metrics (e.g. ambient/behavioural trends) to define the root cause of the evolving deviation. The definition of the root cause triggers the activation of an innovative distributed fuzzy model predictive control engine. The engine allows for shortterm prediction of the building performance outcome (every few minutes) under alternative automated control strategies that aim at mitigating the identified deviation. This is achieved by adapting the operation (self adaptation) of the building to performance targets, while preserving occupants' comfort and health at acceptable levels. Optimization performed through the dynamic assessment engine is an iterative and continuous process that allows for the prompt identification of deviations (continuous assessment through the fault detection/diagnosis) and execution of short loop, few minutes long simulations for the definition of optimal automation strategies. In case no relevant root cause is identified, the problem is passed to the predictive maintenance and retrofitting advisor modules, for further investigation and definition of alternative maintenance and retrofitting actions, respectively, to effectively mitigate the identified deviation. To enhance user experience in this area, a virtual reality environment using BIM information (3D BIM mapping) and mobile device sensors (for location tracking) effectively complements the operation of the maintenance subsystem with a highly intuitive, friendly and useful user interface. The multiscale energy simulation focuses on real time optimization of energy demand and supply with the objective of reducing the difference be tween peak power demand and minimum night time demand. Low level information and intelligence (building) will be uplifted to the high level (district) towards assessing (in real time) the energy performance and demand flexibility at the level of blocks of buildings. This is achieved through an innovative forecasting, aggregation and flexibility module that enables real time dynamic virtual power plants formulation with enhanced aggregated flexibility capabilities to participate in event time locationor price based demand side management strategies.