Energy-based decision engine for household human activity recognition

We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes

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

Proactive Buildings: A Prescriptive Maintenance Approach

Prescriptive maintenance has recently attracted a lot of scientific attention. It integrates the advantages of descriptive and predictive analytics to automate the process of detecting non nominal device functionality. Implementing such proactive measures in home or industrial settings may improve equipment dependability and minimize operational expenses. There are several techniques for prescriptive maintenance in diverse use cases, but none elaborates on a general methodology that permits successful prescriptive analysis for small size industrial or residential settings. This study reports on prescriptive analytics, while assessing recent research efforts on multi-domain prescriptive maintenance. Given the existing state of the art, the main contribution of this work is to propose a broad framework for prescriptive maintenance that may be interpreted as a high-level approach for enabling proactive buildings

RINNO: Towards an Open Renovation Platform for Integrated Design and Delivery of Deep Renovation Projects

The building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. While the majority of the existing building stock has poor energy performance, deep renovation efforts are stymied by a wide range of human, technological, organisational and external environment factors across the value chain. A key challenge is integrating appropriate human resources, materials, fabrication, information and automation systems and knowledge management in a proper manner to achieve the required outcomes and meet the relevant regulatory standards, while satisfying a wide range of stakeholders with differing, often conflicting, motivations. RINNO is a Horizon 2020 project that aims to deliver a set of processes that, when working together, provide a system, repository, marketplace and enabling workflow process for managing deep renovation projects from inception to implementation. This paper presents a roadmap for an open renovation platform for managing and delivering deep renovation projects for residential buildings based on seven design principles. We illustrate a preliminary stepwise framework for applying the platform across the full-lifecycle of a deep renovation project. Based on this work, RINNO will develop a new open renovation software platform that will be implemented and evaluated at four pilot sites with varying construction, regulatory, market and climate contexts

Architectural Simulation of the Integration of Building Information Modelling (BIM) & Business Process Modelling (BPM)

The current methods of building energy simulation that designers and engineers (D&E) use in order to find the energy performance of a building do not take into account the real behavior and daily activi- ties of the people who will use the building. The main aim of this paper is to demonstrate that a system for building simulation, that produces data about the activity behaviour of occupants as members of an enterprise structure and framework, can significantly improve the relevance and performance of building simulation tools, through the study of a real building in daily operation. Furthermore, data (BIM, BPM and occupancy data) has been performed exploiting Open Reference Data Modelling methodology in order to be reusable