The TOPAs open, cloud based platform of decision support tools for building and facilities managers, owners and ESCOs provides a holistic performance audit process that minimise the gap between predicted and actual energy use.
The TOPAs framework for continuous performance auditing allows a better understanding of the actual energy performance in and across existing buildings and facilitates continuous performance improvement based on real operational use.
TOPAs enhances traditional energy performance related data sources (e.g. energy meters, temperature, humidity, weather etc.) with contextual sources such as occupancy models, equipment performance and air quality models to better quantify the performance gap. TOPAs technological objectives include:
KPIs - Enhance current common performance metrics and performance auditing processes for building and blocks of buildings to enable experience and knowledge sharing among stakeholders to firstly improve the replicability of energy savings for similar building typologies through a better base model and secondly investigate the most appropriate business models to foster growth in the energy services sector.
Open BMS approach - the integration of existing technologies to develop an open BMS platform that will efficiently analyses large amounts of data from building to blocks of buildings, including existing building management and metering systems to optimise energy performance and identify areas for improvement.
Energy Prediction - The refinement and fine tuning of building performance modeling approaches to accurately predict energy usage and close the gap between this and actual energy use through enhanced machine-learning approaches. It is envisaged that such models will assist in the identification of energy saving potential, fault detection, and control optimisation within energy performance contracts by providing an independent and accurate measurement and verification tool for Post Occupancy Evaluation (POE).
Model Predictive Control - Integrate enhanced building models with a continuous auditing methodology encapsulating live building performance measurements enabling a measurement based performance evaluation. Improve control and energy consumption at all levels of building operation using Distributed Model Predictive Control (DMPC) approach at building and district level that utilize the occupancy, air quality monitoring and energy prediction model).
Decision Support Tools - Provide decision support tools for building and facilities managers, owners and ESCOs to more effectively manage their site, enhancing visibility on how energy related decisions impact cost, occupant comfort, health and the general management process.
Gap Reduction - Target a reduction in the gap to 10% as an initial benchmark and to progressively challenge this target throughout the project.
Energy Savings - Target additional energy savings in the pilot regions of 15% – 20%.