Energy optimization is a challenge driven by high energy costs and the increasingly stringent regulatory framework. The characterization of inefficiencies in buildings typically requires complex and costly on-site audits performed by energy management experts, hindering the adoption of energy efficiency measures by small and medium-sized energy consumers, among them many SMEs. Our current web-based software customers have requested artificial intelligence capabilities in our software platform allowing personalized recommendations to be issued remotely by the system to end users. RemBAP is the answer to this demand. It will significantly simplify and reduce the implementation and maintenance costs of efficiency optimization and constitutes a strategic development for our company, DEXMA. Energy use optimization has become a priority driven by high energy costs and the increasingly stringent regulatory framework. DEXMA is already helping hundreds of medium-sized end-users worldwide reduce consumption through bill tracking, monitoring of utility meters, data analysis and savings verification with our web based energy management software. As a response to customer demand, this project aims at integrating advanced analytics to our DEXCell software platform allowing for remote recommendations to be issued, significantly simplifying the process and reducing the implementation costs of energy efficiency management. The proposed innovation will allow DEXMA to reach a much higher number of end users, substantially increasing the company’s revenue, creating new jobs and ultimately contributing to large scale energy efficiency. Our main objective for Phase 1 is to evaluate the feasibility of the additional technical developments and to outline the feasibility and strategy for commercialization in different market sectors.

DREEAM aims to demonstrate replicable Net Zero Energy residential building renovation approaches achieving 75% total Net Energy Demand reduction- an improvement of 60% to state of the art market practices. It will utilise packages of interconnected energy systems that achieve a balance between energy efficiency and renewable energy measures. This is enabled by enhancing renewable technologies capacity factors by using advanced building management systems with machine learning algorithms that allow building system integration and auto-optimisation across building dimensions. Currently, deep energy renovations mainly focus on building-level solutions, where, cost-effectively, an energy demand reduction of 40-50% can be achieved. In district scale projects, technologies for energy generation, storage and management can be integrated cost-effectively, reducing up to an additional 25-35% of Net Energy Demand. Multi-owner complexities and need for tailored system integration make this approach location-specific and hard to replicate. A focus on multi-building, single owner situations seeks the appropriate balance for both high energy demand reduction and high market replication potential. Energy Load Management technologies (ICT based building systems management) and services that are successfully piloted in commercial buildings will be adapted to enable this approach. The approach is a breakthrough in reaching widely applicable and replicable pathways for near-NZEB residential building renovation, in particular for social and public housing, which accounts for 12% of European stock, or 25 million units. Building owners that jointly own 160,000 dwellings, energy service engineers and finance experts collaborate to demonstrate the DREEAM approach in 3 climatologically different locations, and initiate replication in 15 more locations within the project duration. Partnership with city-, owner- and innovation networks allows dissemination to 60% of the target market.

Eco-Bot aims to utilize recent advances in chatbot tools and advanced signal processing (i.e. energy disaggregation) using low-resolution smart meter-type data with the goal of changing their behaviour towards energy efficiency. Eco-Bot targets to a personalized virtual energy assistant to deliver information on itemized (appliance-level) energy usage through a chat-bot tool. The "chat-bot" functionality will be use an attractive frontend interface, permitting seamless communication in a more natural and interactive way than a traditional mobile application. This way, Eco-Bot aims to achieve a higher level of engagement with consumers than previous efforts (i.e. serious games, gamification, competitions or other interactive ICT), by adding a more engaging form of interaction with existing platforms that has been proven in different market settings. The proposed system considers knowledge of the delivered multi-factorial models, including rebound-effects, as a result of the baseline research on both European and International activities. Then, based on advanced ICT, such as knowledge engineering, machine learning, expert systems, the project transforms the multi-factorial models for energy reduction to interactive, personalized and targeted recommendations to consumers on how to save energy. Eco-Bot uses also existing NILM, e.g. energy disaggregation methods, and data analytics to break down consumption to the appliance level, where this is possible (smart meters at reasonable granularity, adequate number of information collected) so as to make consumers aware of their most energy-consuming devices. The project will demonstrate the system in three different use cases, each one representing a different business model (B2B / B2B2C /B2C). We aim to validate our system across real and diverse conditions such as socio-cultural, environmental, demographic, climate and consumption, so as to draw concrete conclusions regarding performance, effectiveness, affordability, etc.

RESPOND will aim to deploy and demonstrate an interoperable, cost effective, user centred solution, entailing energy automation, control and monitoring tools, for a seamless integration of cooperative DR programs into the legacy energy management systems. In this endeavour, RESPOND will be leveraged upon an integrated approach for real-time optimal energy dispatching, taking into account both supply and demand side, while exploiting all energy assets available at the site. Owing to its flexibility and scalability, RESPOND solution will be capable of delivering a cooperative demand response at both building and district level. To provide a seamless integration of all DR enabling elements and ensure a high replication potential, RESPOND will be leveraged upon open standards for interoperability with smart home devices and automation systems, connectivity and extendibility towards smart grid and third party services such as for provision of energy prices, weather forecasts, etc. Underpinned by the smart energy monitoring infrastructure, RESPOND will be able to perform reliable energy data analytics and forecasting in order to detect potential energy conservation opportunities, and to adapt, in real time, to the operational environment considering indoor and outdoor conditions, while retaining the requested comfort levels. Through the interaction with the end users, RESPOND will aim to raise their awareness by delivering measurement driven suggestions for energy demand reduction and influence their behaviour making them an active indispensable part of DR loop. In order to demonstrate the high replication potential, RESPOND will target different types of residential buildings, situated in different climate zones, having different forms of ownership (both rental as well as home-owners), population densities and underlying energy systems.

The objectives of the FEEdBACk project are to develop, integrate and trial a wide range of energy focused ICT and behaviour modification applications, that will be used to engage energy users and permit them to understand and change their energy consumption related behaviour in three different built environments (Office/Research Labs, Schools, Domestic smart homes) in three climatic settings (northern Portugal, Mediterranean Spain, and northern Germany). The consortium, from seven EU countries, that has been assembled to deliver FEEdBACk consists of highly experienced research organisations in the energy area; three SMEs that specialise in e-learning digital communication applications targeted at behaviour change and in building energy data gathering and representation to managers and users; and three organisations that own and manage buildings, schools and public and private dwellings, who are committed to lowering and making more sustainable their energy use. The development of this broad spectrum of energy related applications and the utilisation of next generation gamification techniques, tested in such a wide range of built environments has not been attempted before. The outputs from the project will enable accelerated commercialisation, by a wide range of innovative SMEs, within and outside the project consortium, of the tools that will help building owners and stakeholders to understand and reduce their energy consumption. The demonstration of the results of the FEEdBACK project will be valuable for Technical & Market planning for the EU energy industry. The FEEdBACK’s ambition is to enable a wide range of companies to incorporate or integrate elements of the applications and the behaviour change components, thereby making their products more competitive and successful in the EU and on a global scale. This will contribute significantly to achieving important sustainable energy EU objectives.