Scientific research, tech expertise, system integration: a string of different realities united by the common goals of IBIS ECO, an experimental project for a smart system capable of improving the energy efficiency and overall comfort of all types of buildings and installations. All this thanks to a holistic approach integrating technologies (KET, IoT, Data Analytics) and advanced methodologies. The first results are coming in.
The scenario
The increasingly widespread use of air conditioning, lighting and new technologies in buildings has led to a considerable increase in energy demand: in Europe today, almost 40% of final energy consumption, 36% of CO2 emissions and 60% of electricity consumption is caused by the use of buildings.
While the demand for electricity increases dramatically, renewable energies are showing a significant growth that will continue at least until 2050. This means that we are now facing the challenge of integrating renewables into existing networks and infrastructures. Artificial intelligence, machine learning, Internet of Things and other technologies can play a key role in decarbonisation and in general in fostering a rational and more sustainable use of resources.
Particularly in Europe, the most recent directives, such as EPBD (EU) 2018/44, are moving towards a holistic approach to improving energy performance in buildings. A comprehensive data-driven approach that not only looks at the building envelope, but considers and addresses every related aspect: heating, cooling, lighting, ventilation, air quality and thermal and visual comfort.
How the IBIS ECO project was born
IBIS ECO (IoT-based Building Information System for Energy Efficiency & Comfort) is an important experimental project in this scenario.
Co-funded under the Basilicata Region’s ERDF OP in January 2022, led by SCAI LAB, project leader and manager, with the contribution of SCAI Tecno, both companies of the SCAI Group, IBIS ECO sees the involvement of many different actors and competences. These range from theUniversity of Basilicata – DICEM, Department of European and Mediterranean Cultures to the CNR(ISM – Institute of Structure of Matter, and IMAA – Institute of Methodologies for Environmental Analysis), with the participation of Meteo7 Srl, Wish Srl, ECOPRAXI and ENEA – National Agency for New Technologies, Energy and Sustainable Economic Development.
The aim of the IBIS ECO project is to increase and enhance the technological capacity of buildings to interact with users, system operators and the energy infrastructure in various areas: in a word, Smart Readiness (the SRI, Smart Readiness Indicator, is the common EU index for assessing the readiness of buildings to use smart technologies, introduced by the EU Directive 2018/844 on the Energy Performance of Buildings (EPBD).
Thanks to the implementation of hi-tech pollutant gas sensors, the provision of KET (Key Enabling Technologies), IoT and Data Analytics, any anomalies present are detected at an early stage and predictive analyses are carried out to optimise the management of buildings and facilities.
IBIS ECO: how it works
IBIS ECO is a smart platform for monitoring and managing the energy performance of existing buildings and represents a unique set of functionalities in today’s market: it integrates information from different sources (sensors, weather forecasts, consumption data, user feedback) through advanced solutions that guarantee security and ease of installation even in existing buildings, without the need for wiring.
The creation of the building monitoring and management system is based on a series of integrations:
• New low-cost, high-sensitivity sensors for environmental comfort and micro-sensors made with sensitive materials that are more advanced than the sensors on the market today;
• Non-invasive and easy to manage and install IoT monitoring infrastructures that integrate the necessary sensors for multi-parameter monitoring (including standard temperature, humidity, air speed and lighting) and certified data transmission
• Innovative use of indoor microclimate data and data on the weather conditions outside the building, both current and forecast, using evolutionary forecasting models to plan interventions and plant operating conditions;
• Cloud-based decision support platform for Energy and Comfort, functional for the elaboration of comprehensive energy plans.
The Smart Energy Analytics models and algorithms implemented by the building guarantee:
• Increased indoor well-being for the benefit of the building’s end users, with particular attention to heating/cooling, lighting, air quality and ventilation requirements, thanks to a better overall calibration of the systems’ operating modes;
• Timely identification of abnormal operating conditions, also useful for activating plant management operators in advance, preventing breakdowns or more serious deterioration
• Optimisation of building maintenance in support of the facility manager, to improve the cost-effectiveness of ordinary and extraordinary operation and maintenance, with reference to the Life Cycle Assessment of systems;
• Implementation of specific dashboards and smart applications to foster greater end-user awareness in the use of all building energy systems
Expected Results
The project will conclude at the end of 2024, with the experimental validation of the approach on demonstrators installed on two experimental sites with different characteristics of use, structure, climatic and territorial conditions: at the University Campus of Matera and at a school in the municipality of Montemurro in the Agri Valley.
Guidelines will then be issued, derived from the applied models and validated in the experimental exercise of the demonstrators, providing good practices and indicators for the management of the whole system.
A useful set of data to meet the needs of energy and facility managers, operators and maintenance staff and the building users themselves.
With the collection of several parameters using excellent hardware and software sensor solutions, it will be possible to define policies aimed at optimising energy consumption and at the same time maintain optimal conditions of indoor environmental comfort; enhance the overall information heritage generated by the building and integrated with the knowledge of the area’s microclimate; introduce advanced functions for the early detection of critical situations, predictive maintenance and support for the design of new interventions.
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