Stelios Krinidis

enCOMPASS — An integrative approach to behavioural change for energy saving

This paper presents the research objectives of the enCOMPASS project, which aims at implementing and validating an integrated socio-technical approach to behavioural change for energy saving. To this end, innovative user-friendly digital tools will be developed to 1) make energy data consumption available and understandable for different types of users and stakeholders (household residents, office employees, school pupils, building managers, utilities, ICT providers) and to 2) empower them to collaborate in order to achieve energy savings and manage their energy needs in efficient, cost-effective and comfort-preserving ways. The project will demonstrate how this can be achieved with a novel approach that integrates user-centered visualisation of energy data from smart sensors and user-generated information with context-aware collaborative recommendations for energy saving, intelligent control and adaptive gamified incentives enabling effective and sustained behavioural change.

GreenSoul∗ a novel platform for the reduction of energy consumption in communal and shared spaces

In order to achieve higher energy efficiency in buildings, it is essential that devices be consciously used. The GreenSoul project aims to build energy awareness to users and help them change their energy consumption behavior. This will be succeeded by changing the way people use energy consuming devices and by embedding intelligence in the devices themselves, which could autonomously decide about their operation mode and energy consumption. GreenSoul research and innovation focus on the reduction of the energy consumption above 20%, within the domain of public buildings or buildings of public use. GreenSoul forecasts that it is possible to surpass this target providing that a true collaboration among people, devices and buildings is achieved by putting in place the range of technological contributions set forward by this project.

Human tracking & visual spatio-temporal statistical analysis

In this work, a novel, multi-space, real-time and robust human tracking system is going to be presented. The system exploits a multi-camera network monitoring the multi-space dynamic environment under interest, detecting and tracking the humans in it. The system is able to handle the dynamic changes of the environment, as well as partial occlusions utilizing virtual top cameras. Furthermore, the system is able to real-time visualize the detection and tracking results on the architectural map of the dynamic environment, as well as a variety of statistics. The visual spatio-temporal analysis of the tracked data are presented in a consolidated form for the overall monitoring area and analytically for each space separately and for each tracked human. These statistics could be also combined with the energy consumption in the area, as well as with other environmental data providing semantic information such as comfort. The overall system is equipped with a number of visual interactive tools providing real-time spatio-temporal human presence analysis offering to the user the opportunity to capture and isolate the areas/spaces with high human presence, the days and times of high human presence, to correlate this information with the potential energy consumption and indicators such as comfort.

A building performance evaluation & visualization system

A novel big data building performance evaluation knowledge processing and mining system utilizing visual analytics is going to be presented in this paper. A large dataset comprised of building information, energy consumption, environmental measurements, human presence and behavior and business processes is going to be exploited for the building performance evaluation. Building performance evaluation is one of the most important factors in engineering that leads to building renovation and construction with low energy consumption and gas emissions in conjunction with comfort, utility and durability. For this purpose, business processes occurring in the building are correlated with the energy consumption and the human flows in the spatiotemporal domain modeling the dynamic behavior of the building. These models lead to the extraction of useful semantic information and the detection of spatiotemporal patterns that are important for the evaluation of the building performance. Furthermore, a number of novel visual analytics techniques allow the end-users to process data in different temporal resolutions and with different temporal filters, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed visual analytics techniques support design and energy management decisions by visualizing the building measurements regarding business and comfort aspects. To do so, the proposed system includes a variety of techniques and components, properly selected to offer quick identification of focal points and evaluation of the building performance. Considering the increasing interest and the green building goals of almost all world governments including EU, the suggested methodology and application could be rendered a very useful tool for the Architecture and Engineering Community working on Building Performance Simulation and Analysis, and all related communities in Architect, Engineering and Construction (AEC) industry.

Comparison of Detailed Occupancy Profile Generative Methods to Published Standard Diversity Profiles

Occupancy schedules in building spaces play an important role in evaluating a building’s energy performance. This work seeks to identify disparities between different occupancy estimation techniques; standardised occupancy profiles found in literature, business processes’ based profiles through interviews and accurate profiles from real on-field measurements. The occupancy diversity profiles of secondary spaces in a healthcare facility building are analysed through descriptive statistics and t test methods over different time horizons. Occupancy measurements are obtained by utilising a novel, robust and highly accurate real-time occupancy extraction system which is established through a network of depth cameras. Results indicate that the utilisation of real occupancy data, along with elaboration of the business processes that take place in building spaces have the potential to support more precise profiles in Building Performance Simulation software tools.

GreenSoul: An IoT Platform for Empowering Users’ Energy Efficiency in Public Buildings

The GreenSoul (GS) framework aims to provide a low-cost energy-efficient Information and Communications Technology (ICT) platform which seamlessly augments a traditional public-use building with a set of assets (apps, interactive interfaces, device adaptors, smart meters and a Decision Support Engine), which mediate in the interactions of users with their environments and the energy consuming devices or systems present in them. GreenSoul envisions public use buildings as ecosystems of GreenSoul-ed devices which cooperate with other devices, standard Smart Meters and, very importantly, with eco-educated and eco-aware users to minimize the unnecessary energy consumption. GS architecture is supported by a socio-economic behavioural model, which aids on behaviour understanding to turn energy consuming devices into active pro-sustainability agents that manifest to their surrounding users how well or badly they are being manipulated (energy-wise), offer tips about how to use them more efficiently and even adapt their own functioning to avoid energy waste.