Abstract
The implementation of efficient demand response (DR) programs for household electricity consumption would benefit from data-driven methods capable of simulating the impact of different tariffs schemes. This paper proposes a novel method based on conditional variational autoencoders (CVAE) to generate, from an electricity tariff profile combined with weather and calendar variables, daily consumption profiles of consumers segmented in different clusters. First, a large set of consumers is gathered into clusters according to their consumption behavior and price-responsiveness. The clustering method is based on a causality model that measures the effect of a specific tariff on the consumption level. Then, daily electrical energy consumption profiles are generated for each cluster with CVAE. This non-parametric approach is compared to a semi-parametric data generator based on generalized additive models. Experiments in a publicly available data set show that, the proposed method presents comparable performance to the semi-parametric one when it comes to generating the average value of the original data (13% difference in root mean square error). The main contribution from this new method is the capacity to reproduce rebound and side effects in the generated consumption profiles. Indeed, the application of a special electricity tariff over a time window may also affect consumption outside this time window. Another contribution is that the proposed clustering approach is capturing the reaction to a tariff change. When compared to a clustering method with classical features (min, max and average consumption), the improvement in the Calinski-Harabasz index was 128% for consumers associated with tariff changes.

Abstract
This paper provides a survey of big data analytics applications and associated implementation issues. The emphasis is placed on applications that are novel and have demonstrated value to the industry, as illustrated using field data and practical applications. The paper reflects on the lessons learned from initial implementations, as well as ideas that are yet to be explored. The various data science trends treated in the literature are outlined, while experiences from applying them in the electricity grid setting are emphasized to pave the way for future applications. The paper ends with opportunities and challenges, as well as implementation goals and strategies for achieving impactful outcomes.

Abstract
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.

Abstract
This paper proposes a Local Energy Market using a P2P blockchain-powered marketplace where agents bilaterally trade energy after the consumption and production period, and not before, as usual in electricity market design. The EU and MIBEL regulatory framework for Renewable Energy Communities potentially creates space for such a market, but some improvements in the settlement procedures and agent's participation must be met. © 2020 IEEE.

Abstract
The growing digitalisation, grid complexity and the number of digitally connected devices that communicate with systems in the distribution grid are enabling the continuous development of automation and intelligence, acquisition of data from sensors, meters and devices for monitoring and managing the distribution network, to achieve an enhanced, preventive, resilient and flexible network operation philosophy. This study presents a set of use cases towards the demonstration of the benefits of implementing a platform that collects, aggregates and facilitates horizontal integration and data correlation from various sources, enabling these use cases across the distribution grid. The adequacy analysis of current distribution network architecture considered derived requirements on the characterisation of its evolution taking advantage of key digital technologies, towards the implementation of distributed control and management strategies. It is also presented a benefit analysis of implementing a centralised common data and service platform, i.e. an internet of things (IoT) platform, regarding new functionalities and applications.

Abstract
This study offers an overview of the H2020 InterConnect project, which targets the relation between smart homes and distribution grids. The project vision is to produce a digital marketplace, using an interoperable marketplace toolbox and Smart appliances REference Ontology (SAREF) compliant Internet of Things (IoT) reference architecture as the main backbone, through which all SAREF-ized services, compliant devices, platform enablers and applications can be downloaded onto IoT and smart grid digital platforms. Energy users in buildings, either residential or non-residential, manufacturers, distribution grid operators and the energy retailers will work together towards the demonstration of the smart energy management solutions in seven connected large-scale test-sites in Portugal, Belgium, Germany, the Netherlands, Italy, Greece and France. This study depicts how InterConnect project will enhance the relation and the interconnectivity between smart buildings and grids safeguarding the definition of the role of each stakeholder in energy and non-energy services.