TOPAs adopts the principle of continuous performance auditing and considers not only energy use but also an understanding of how buildings are used and their climatic state, thus providing a holistic performance audit process through supporting tools and methodologies that minimise the gap between predicted and actual energy use.
As shown in Figure 1 (attached below) TOPAs is based on a cognitive loop methodology, in essence it is a system that senses, learns, acts, and operates. Data is abstracted from the environment, by leveraging IoT technologies to transform data into actionable insights in order to better utilise assets and manage blocks of buildings.
The TOPAs Core is designed to facilitate easy implementation in any building by leveraging the BMS protocols implemented in the TOPAs RTU/LINC secure edge connector with easy configuration and setup using the TOPAs ‘cookbook’ methodology. Unlike other solutions that have already smart building IT systems in place (e.g. the Galeo building, where the BMS directly provides web services), TOPAs allows retrofitting of regular buildings enabling the introduction of advanced services provided by the TOPAs oBMS platform.
The TOPAs core provides an integrated platform with reduced complexity and a cost-effective means of abstracting systems information, where the facility manager can monitor and control the building through a single cloud-based Open Building Management System (oBMS) platform. The TOPAs add-on services provide modelling, predictive control, fault detection, system reconfiguration and air quality analysis to provide a full suite of tools for building energy management.
The core functionality of TOPAs is focused on data-abstraction, i.e. the components including LINC (Connectors & Resources), RTU (secure host at the edge), oBMS (Watson IoT, OpenAPI), NIM (meta-model) and HMI for data visualisation as depicted in Figure 2 (attached below). Additional TOPAs components (Modelling and Decision Support) are available as Add-on Services to the core, see Figure 3 (attached below).
The models developed within TOPAs play a significant role in effectively managing blocks of buildings. These include semantic models of blocks of buildings (NIM) i.e. meta-data on sensors (e.g. their semantic type) and the building assets (e.g. air handling units) as well as locations (e.g. rooms). Models for energy prediction and occupancy are required to understand the building usage and the impact changes to the operational strategy have on actual energy usage. Model predictive control strategies are used to drive the operational strategies for thermal and electrical regulation within and across blocks of buildings. The decision support tools provide additional insights to building managers/owners to ensure optimized performance is maintained across buildings and blocks of buildings. These include fault detection and diagnosis (FDD) tools, environment monitoring tools, configuration and calculation of key performance indicators, and a common interface for monitoring energy flows.
