Information and communication technologies (ICT) offer immense potential to improve the energetic performance of buildings. Additionally, common building control systems are typically based on simple decision-making tools, which possess the ability to obtain controllable parameters for indoor temperatures. Nevertheless, the accuracy of such common building control systems is improvable with the integration of advanced decision-making techniques embedded into software and energy management tools. This paper presents the design of a building energy management system (BEMS), which is currently under development, and that makes use of artificial intelligence for the automated decision-making process required for optimal comfort of occupants and utilization of renewables for achieving energy-efficiency in buildings. The research falls under the scope of the H2020 project BREASER which implements fuzzy logic with the aim of governing the energy resources of a school in Turkey, which has been renovated with a ventilated façade with integrated renewable energy sources (RES). The BRESAER BEMS includes prediction techniques that increase the accuracy of common BEMS tools, and subsequent energy savings, while ensuring the indoor thermal comfort of the building occupants. In particular, weather forecast and simulation strategies are integrated into the functionalities of the overall system. By collecting the aforementioned information, the BEMS makes decisions according to a well-established selection of key performance indicators (KPIs) with the objective of providing a quantitative comparable value to determine new actuation parameters.

Monitoring systems are one of the pivotal diagnosis tools for the analysis of the energy performance in buildings. They provide on-site information and support the decision making process. Moreover, monitoring systems allow the calculation of energy and comfort indicators, despite, large buildings do not provide a single sensor network. So, with the aim of integrating multiple monitoring systems to support the energy assessment, this paper presents a combination of thereof into a nearly Zero Energy Building in order to evaluate a set of Key Performance Indicators. This study demonstrates the importance of monitoring systems and how they support the automatic calculation of aggregated values in terms of indicators to determine the building performance, thus detecting the malfunctioning of the facilities so as to reduce the energy consumption and CO2 emissions.

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