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International audience; Energy Smart Appliances provides utilities and appliance manufacturers, and designers with new approaches to better understand real-world performance, assess actual energy benefits, and tailor each technology to the needs of their customers.With contributions from a fully international group of experts, including heads of prestigious research organizations and leading universities, and innovation managers of the main appliance manufacturers, Energy Smart Appliances includes discussion on:Enabling technologies for energy smart appliances, covering IoT devices and technology and active energy efficiency measures in residential environmentsSmart home and appliances, answering questions like ‘Where are we heading in terms of the overall smart homes’ future?’ and ‘What’s the energy impact from smart home devices?’Demand-side management and demand response, covering overall system/ appliances readiness and ideal energy management scenario to drive demand responseEnergy smart appliances’ best practices and success stories, including refrigerators, washers, dryers, and moreWith practical coverage of a wide range of potential scenarios and existing and future challenges, Energy Smart Appliances is an essential learning resource for electrical engineering professionals, equipment manufacturers, and designers, along with postgraduate electrical engineering students and researchers in related fields and programs of study.

The European Commission has promoted the deployment of the Digitalisation of Energy Action Plan (DoEAP), in order to develop an efficient, competitive market for a digital energy infrastructure and digital energy services that are both cyber-secure and sustainable. A central aspect of DoEAP is represented by the concept of Energy Data Spaces. Data exchange is crucial for emerging energy data services in the digital energy market and will help suppliers and energy service providers to innovate and cope with an increasing share of renewables in a more decentralised energy system. The data includes metering data, data from consumers such as home appliances, building automation, EV charging stations, or prosumers PV panel & inverters. Its availability and timely sharing and use among the relevant players is key for the energy transition. This document addresses main issues of data exchange in the three interconnected key sectors: energy, buildings and mobility; the analyses focus on existing concepts of data formats and data standards, reflecting on how to facilitate data sharing across the different sectors based on a common data framework. The foremost use cases of European projects and initiatives in the specific sector or at cross-sector level are presented, depicting the current state of data exchange deployments and identifying the necessary actions for the upcoming developments.

The Horizon2020 InterConnect project, consisting of 50 European entities from eleven countries, aims to improve interoperability in the Smart home and office domain with the electricity sector. SAREF is an ontology created to enable interoperability between smart devices. However, there is a lack in the literature of practical examples to implement SAREF in a realistic setting. We validate the practical implementation of SAREF on two aspects. Firstly we investigate the opportunities of having various data available as a SAREF knowledge graph in a Machine Learning (ML) context. We do this by transforming tabular data containing sensor data into SAREF and demonstrating the communication capabilities of IoT devices when represented as such. We furthermore demonstrate opportunities for rule-based smart home scenario handling using OWL and SWRL languages. We finally transform elements from the SAREF knowledge graph to common ML data formats. Secondly we explore how to increase the explainability of ML algorithms in a Smart home context. We surveyed multiple members from the InterConnect consortium that are involved in developing Smart home applications. We question them about the usage of ML models and its explainability aspects. Also we explored the explainability benefits using Jupyter notebooks for controlling and visualizing the internal steps of ML algorithms applied to Smart home data. We plan further experiments in an extensive pilot within the InterConnect project, consisting of almost 200 new apartments within the Netherlands that will be outfitted with smart devices expressing their data compatible with SAREF &nbsp