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Identifying the parameters of grey-box models requires enough data collected from sensors installed inside and outside of the building for long enough period of time. Consequently, this process is time consuming, costly especially in large buildings that require more sensors, and can only be conducted after the building is constructed. This paper introduces a procedure for identifying greybox models from white-box models. Following this procedure, grey-box models can be identified using data generated by a white-box model, without any requirement for mounting sensors in a building. This reduces the cost and time of modelling, control design and prediction. The introduced procedure is utilized to find a grey-box model of the heat dynamics of a four-floor building in Spain. Simulation results demonstrate the effectiveness of this procedure.

The Energy Performance Gap (EPG) is a phenomenon that both can be encountered in new and in refurbished buildings and potentially jeopardises the effort of making buildings more sustainable. Often, buildings are affected by a more generic “performance gap”, meaning that, beside the EPG, also the comfort delivered to the occupants is not as requested. In this work, we present a Human-in-the-Loop (HuiL) approach to control buildings. We developed a mobile app that allows occupants to provide their personal feedback about their indoor thermal sensation in real time, while monitoring their actual location within the building. Based on the thermal sensation votes, the system is capable of controlling the settings of smart thermostats in each room of a building. We tested the mobile app and a preliminary feedback-based manual control of the thermostats in a school building located in the Greater Copenhagen Area. Preliminary results show a higher comfort, when using the HuiL perception-based control approach: in particular, the answer “good”, used to positively rate the indoor temperature, was chosen 50,9% of times in a period with standard set points, and 63,3% of times in a periodwhile the set points of the smart thermostats of the classrooms were chosen based on a HuiL perception-based control.

The energy signature (ES) model is a well-known method to measure the energy performance of a building. This method uses coarse data aggregation that omits valuable information about the dynamics of the thermal elements of a building. This work presents a method to reconstruct these hidden dynamics without increasing the measuring complexity. The proposed method uses a state space formulation of the ES, that is filtered using hourly measurements of outdoor temperature and heat consumption. It is shown that the reconstructed state variable works as a proxy of the indoor temperature, and reacts to the weather conditions. These results highlight the potential of this method for real-time heating performance evaluation, as well as anomaly detection.

The purpose: CLIMIFY aims at maximizing the occupants’ experience in the built environment and at enhancing buildings’ performance. We monitor the indoor environment by using data from different sources. Moreover, we collect, directly from the occupants, feedback on the indoor climate. Finally, we control the indoor climate based on the occupancy levels and the real needs of occupants. We enhance the comfort, maximize the occupants’ performance, wellbeing and health, save energy, and actively create flexibility that can be offered to the energy grids. The Problem: Most buildings do not perform as they should. Old buildings, new buildings, retrofit buildings: most of them have issues with indoor environmental quality (IEQ) and energy efficiency. Moreover, the use of renewable energy sources challenges the energy grids: Buildings could play an important role in the near future and help in balancing the energy grids. However, they are still planned as in the sixties, where the usage of buildings was ruled by fixed schedules and modular energy production. BMS systems are usually closed and cannot englobe innovation that arrives in form of IoT sensors and actuators; finally, system from different vendors do not work together. Climify proposes a viable solution to accelerate a smartification of the building stock.

There are international activities and on-going initiatives, particularly at the European level, to define what Positive Energy Districts should be, as the driving concept for the urban transition to a sustainable future. The first objective of the paper is to contribute to the on-going and lively debate about the definition of the notion of Sustainable Plus Energy Neighbourhood (SPEN), which highlights the multiple dimensions when talking about sustainability in districts moving beyond the traditional and strict building energy assessment. Based on a holistic methodology which ensures the consideration of the multidimensional nature and goals of SPEN, the paper outlines an evaluation framework. The evaluation framework defines the key performance indicators distributed in five categories that consider energy and power performance, GHG emissions, indoor environmental quality, smartness, flexibility, life cycle costs and social sustainability. This framework is designed to be implemented during integrated design processes aiming to select design options for a neighbourhood as well within during the operational phase for monitoring its performance. Further work will include the implementation and validation of the framework in four real-life positive energy neighbourhoods in different climate zones of Europe as part of syn.ikia H2020 project.

Within the simulation of buildings’ energy performance, Occupant Behavior is often modelled only through fixed and repeated profiles. The incorrect simulation of occupant behavior can however lead to the so called Energy Performance Gap both in new and retrofitted buildings. In this work, we demonstrate how integrating real occupants’ profiles related to the opening and closing of windows can positively affect the results of the dynamic performance simulation of buildings. Within this scope, we modelled (Dymola, Modelica) two 10-apartments buildings and their HVAC systems, and simulated those under two main scenarios, one with a fixed air change rate through windows, one including opening and closing cycles as monitored in the real building. Including realistic windows’ ventilation can improve the simulation results, but a holistic occupant behavior is highly necessary to get reliable and accurate buildings’ simulations.

Real energy performance of new and retrofitted buildings often consistently differs from expectations. While occupants might complain about poor indoor climate, the energy use in such buildings is often higher than expected, leading to the well-known phenomenon called “Energy Performance Gap”. In the past years, monitoring of buildings, both in terms of energy use and indoor climate conditions, was realised mostly for office buildings only, and at high financial costs. However, the exponential growth in availability of IoT devices, over the last years, opens now new scenarios for low-cost monitoring and control solutions of buildings. Yet, modern IoT devices are often only accessible online through the vendors’ software, although some devices make use of open communication protocols and can, therefore, be connected to open platforms such as openHAB. However, the use of open platforms is still connected to a big efforts for many final users. We, therefore, propose climify.org, an open platform for plug and play connection of IoT sensors and actuators, for easy monitoring and controlling of buildings and buildings’ HVAC systems. The platform climify.org offers, at time of writing, three main applications. The first application is an IoT device installation app, to be used on portable devices (e.g. mobile phones or tablets of system administrators): this app allows easily installing and locating a sensor or an actuator, within a building. The second application is an online service for data visualisation and HVAC control: while the monitoring data can be plotted, the service offers several data evaluation methods; moreover, the settings of the connected actuators can be modified and controlled. The third application can be installed on portable devices (mobile phones and tablets of buildings’ occupants) and allows occupants to provide feedback on their perception of the indoor climate through several questionnaires’ formats. Through the three applications developed wit