Services and business models based on the operational stage of buildings aims at monitoring and improving their energy performance. Predictive capabilities related to comfort evaluation, energy demand, consumption or generation, will be complemented by optimization capabilities for the management of comfort-aware building energy consumption, management of District Heating networks or energy matching.
Facility and Resources Fingerprinting for Efficiency and Optimal Balancing of Energy Vectors
BTC is one of Europe’s largest shopping, entertainment, business, commercial, and logistics centers. It is located in the urban catchment of Ljubljana, Slovenia, has a total gross area of 475,000 m2, and includes shopping malls, multiplex cinema, bars and restaurants, hotel, waterpark, entertainment center and high-rise office buildings. MATRYCS will enable facility manager efficient management of appliances and subsystems that corresponds to local context and is coordinated on a system level. Utilizing historical data and applying the non-intrusive fingerprinting approach to live data streams, the aim is to aid the traditional monitoring systems with reasoning capabilities and thereby evaluate value for holistic BMS. Fingerprinting represents a necessary first step in the realization of next generation BMS that will allow realizing higher service efficiency for various energy vectors and scopes of facility management.
Smart building comfort-aware predictive energy management and coordination with smart grids and local RES generation
The available pilot infrastructure consists of the ASM-owned microgrid, which includes ASM Offices Headquarter smart building. The HVAC system, heat pumps for space heating /cooling (overall 120-140 KW load), together with the lighting subsystems can be controlled individually and/or overall, via a legacy BMS (DELTA). The building also includes local intermittent generation from PVs (peak power 180 KW), a second-life battery storage (120 KW) as deployed within the ELSA project, and one EVs 22 KW recharging station. 20-30 IoT smart meters are deployed at the premises of some residential and small industrial energy consumers, which provides near real time digital energy consumption measurement from business and residential consumers, two concentrator-level PMUs, two secondary substations equipped with RTUs and other digital sensors, deployed with a view to increase the LV network observability. Supported by such facilities, this pilot aims at deploying a cross-stakeholder (Building/facility Manager/ESCO/retailer and DSO) use case and to validate the capability of the MATRYCS reference Architecture to provide seamless interoperability among data made available via legacy BMS plug-ins, IoT and smart electricity meters.
Services for the better management of self-production systems
COOPERNICO has a significant number of members and operates as electricity supplier. The participants in this pilot will be chosen from COOPERNICO’s members (~850 users). At least half of them have a supplier contract with COOPERNICO and one-third are prosumers. This pilot aims at creating a service to help its members in order to better management their self-production system (for prosumers) and to improve the energy performance of COOPERNICO members’ households (for the members with supplier contract). COOPERNICO’s pilot proposes the use of data coming from its members’ smart meters regarding their energy production and match it to their real electricity consumption. This information will be correlated to the type of building, year of construction and location. This will be the data set to design a service where advice is given on how to better use the energy produced, consumed and what improvements can be done to the building itself to improve their energy efficiency. This service could be used by other members who only consume electricity from the grid, giving all data with the exception of the data regarding the production units, and to advise them on how they can improve their building or energy use or to help them tackle energy poverty.
Energy Demand Prediction to design and develop DHN and optimize the operation
VEOLIA in this pilot, at a district scale, is covering the role of district heating Facility Manager (FM), aiming at generating analytics to improve FM processes. This pilot involves a District Heating Network (DHN) providing heating and domestic hot water to 1,500 households with 19 distribution substations in Laguna de Duero (Spain). This is a relevant facility where the significance of the data at district level can be easily demonstrated. The DHN needs information coming from individual apartments (energy demand) from a baseline (static data) as well as in operation (real data). The involvement of individual data from the residential sector is crucial to achieve this. The aggregation of such demand (simulated for designing purpose or measured for operational reason) is needed. The operation of the generation and distribution, how to match the demand to the generation, the prediction taking into account weather conditions or other kind of social event can contribute to economic benefit. This pilot will be in charge of applying the MATRYCS framework to optimize the operation of this DHN, in order to create economic and environmental benefits. It will be also in charge of applying the MATRYCS framework to study both the addition of new buildings to the existing DHN and the implementation of new energy conservation measures. Information and data from the current DHN (Veolia Hubgrade) will calibrate new models and simulations of alternative scenarios, which will be created using new components and equipment extracted from e-catalogues. This FM use case will be responsible for demonstrating how analytics based on data will contribute to achieving an economic benefit optimization as well as the reduction of energy consumption in a DHN.