An Intelligent Smart Plug with Shared Knowledge Capabilities

The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource's context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 and SFRH/BD/109248/2015.info:eu-repo/semantics/publishedVersio

Energy Resources Management Enabled by Internet of Things Devices

The participation of small end-users in the smart grid brings benefits for the end-users and for the smart grid. This paper will treat end-users using communities involving energy sharing between private buildings (residential and commercial) and public buildings. The energy can be shared among end-users and the community can be managed centralized. The paper uses IoT devices to enable the active participation of end-users. The use of this type of devices is growing and more and more market available product are appearing. The remote control and monitor capabilities, provided by the normality of IoT devices, can and should be used in energy management systems as enablers. This paper uses IoT devices, located in end-users, to enable the participation of these player in the community. The paper will propose a smart energy community platform and show its results.The present work was done and funded in the scope of the following projects: European Union's Horizon 2020 project DOMINOES (grant agreement No 771066), and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Forecasting Refrigerators Consumption to Support their Aggregated Participation in Demand Response

Demand response programs have become very relevant. However, one of the important facts to have a reliable DR program is the creation of a clear and trustable perspective of the load consumption during the upcoming time periods. On another hand, the increment of the energy-based systems and different energy consuming appliances in the last decades results in larger daily energy consumption which creates the unpredictability of the energy demand. This variety of consumption profiles requires not only consideration of the total consumption of each consumer, but more detailed and focused studies on each type of energy consuming devices. Therefore, this paper proposes a system containing a combination of different forecasting and clustering algorithms to predict the power consumption of several refrigerators and aggregate them into certain groups based on the characteristics of their consumption profiles. Sequentially, the obtained results will be aggregated and serve as basis in order to schedule refrigerators in the context of a demand response program. In the case-study, 20000 refrigerators are considered.This work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224) and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.info:eu-repo/semantics/publishedVersio

Privacy-preserving overgrid: Secure data collection for the smart grid

In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings

Privacy-preserving Overgrid: Secure Data Collection for the Smart Grid

In this paper we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation ( extit{PP-Overgrid}). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of ``flexible'' energy consumption, i.e. the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildin

Use of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approach

This article belongs to the Special Issue Sensors for Smart GridsThe increase in sensors in buildings and home automation bring potential information to improve buildings' energy management. One promissory field is load forecasting, where the inclusion of other sensors' data in addition to load consumption may improve the forecasting results. However, an adequate selection of sensor parameters to use as input to the load forecasting should be done. In this paper, a methodology is proposed that includes a two-stage approach to improve the use of sensor data for a specific building. As an innovation, in the first stage, the relevant sensor data is selected for each specific building, while in the second stage, the load forecast is updated according to the actual forecast error. When a certain error is reached, the forecasting algorithm (Artificial Neural Network or K-Nearest Neighbors) is trained with the most recent data instead of training the algorithm every time. Data collection is provided by a prototype of agent-based sensors developed by the authors in order to support the proposed methodology. In this case study, data over a period of six months with five-minute time intervals regarding eight types of sensors are used. These data have been adapted from an office building to illustrate the advantages of the proposed methodology.This work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of ITEA 3 SPEAR Project 16001 and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017info:eu-repo/semantics/publishedVersio

A Residential House Comparative Case Study Using Market Available Smart Plugs and EnAPlugs with Shared Knowledge

Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.The present work has been developed under Project SIMOCE (ANI|P2020 17690), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019 and SFRH/BD/109248/2015.info:eu-repo/semantics/publishedVersio

μGIM - Microgrid intelligent management system based on a multi-agent approach and the active participation of end-users

[ES] Los sistemas de potencia y energía están cambiando su paradigma tradicional, de sistemas centralizados a sistemas descentralizados. La aparición de redes inteligentes permite la integración de recursos energéticos descentralizados y promueve la gestión inclusiva que involucra a los usuarios finales, impulsada por la gestión del lado de la demanda, la energía transactiva y la respuesta a la demanda. Garantizar la escalabilidad y la estabilidad del servicio proporcionado por la red, en este nuevo paradigma de redes inteligentes, es más difícil porque no hay una única sala de operaciones centralizada donde se tomen todas las decisiones. Para implementar con éxito redes inteligentes, es necesario combinar esfuerzos entre la ingeniería eléctrica y la ingeniería informática. La ingeniería eléctrica debe garantizar el correcto funcionamiento físico de las redes inteligentes y de sus componentes, estableciendo las bases para un adecuado monitoreo, control, gestión, y métodos de operación. La ingeniería informática desempeña un papel importante al proporcionar los modelos y herramientas computacionales adecuados para administrar y operar la red inteligente y sus partes constituyentes, representando adecuadamente a todos los diferentes actores involucrados. Estos modelos deben considerar los objetivos individuales y comunes de los actores que proporcionan las bases para garantizar interacciones competitivas y cooperativas capaces de satisfacer a los actores individuales, así como cumplir con los requisitos comunes con respecto a la sostenibilidad técnica, ambiental y económica del Sistema. La naturaleza distribuida de las redes inteligentes permite, incentiva y beneficia enormemente la participación activa de los usuarios finales, desde actores grandes hasta actores más pequeños, como los consumidores residenciales. Uno de los principales problemas en la planificación y operación de redes eléctricas es la variación de la demanda de energía, que a menudo se duplica más que durante las horas pico en comparación con la demanda fuera de pico. Tradicionalmente, esta variación dio como resultado la construcción de plantas de generación de energía y grandes inversiones en líneas de red y subestaciones. El uso masivo de fuentes de energía renovables implica mayor volatilidad en lo relativo a la generación, lo que hace que sea más difícil equilibrar el consumo y la generación. La participación de los actores de la red inteligente, habilitada por la energía transactiva y la respuesta a la demanda, puede proporcionar flexibilidad en desde el punto de vista de la demanda, facilitando la operación del sistema y haciendo frente a la creciente participación de las energías renovables. En el ámbito de las redes inteligentes, es posible construir y operar redes más pequeñas, llamadas microrredes. Esas son redes geográficamente limitadas con gestión y operación local. Pueden verse como áreas geográficas restringidas para las cuales la red eléctrica generalmente opera físicamente conectada a la red principal, pero también puede operar en modo isla, lo que proporciona independencia de la red principal. Esta investigación de doctorado, realizada bajo el Programa de Doctorado en Ingeniería Informática de la Universidad de Salamanca, aborda el estudio y el análisis de la gestión de microrredes, considerando la participación activa de los usuarios finales y la gestión energética de lascarga eléctrica y los recursos energéticos de los usuarios finales. En este trabajo de investigación se ha analizado el uso de conceptos de ingeniería informática, particularmente del campo de la inteligencia artificial, para apoyar la gestión de las microrredes, proponiendo un sistema de gestión inteligente de microrredes (μGIM) basado en un enfoque de múltiples agentes y en la participación activa de usuarios. Esta solución se compone de tres sistemas que combinan hardware y software: el emulador de virtual a realidad (V2R), el enchufe inteligente de conciencia ambiental de Internet de las cosas (EnAPlug), y la computadora de placa única para energía basada en el agente (S4E) para permitir la gestión del lado de la demanda y la energía transactiva. Estos sistemas fueron concebidos, desarrollados y probados para permitir la validación de metodologías de gestión de microrredes, es decir, para la participación de los usuarios finales y para la optimización inteligente de los recursos. Este documento presenta todos los principales modelos y resultados obtenidos durante esta investigación de doctorado, con respecto a análisis de vanguardia, concepción de sistemas, desarrollo de sistemas, resultados de experimentación y descubrimientos principales. Los sistemas se han evaluado en escenarios reales, desde laboratorios hasta sitios piloto. En total, se han publicado veinte artículos científicos, de los cuales nueve se han hecho en revistas especializadas. Esta investigación de doctorado realizó contribuciones a dos proyectos H2020 (DOMINOES y DREAM-GO), dos proyectos ITEA (M2MGrids y SPEAR), tres proyectos portugueses (SIMOCE, NetEffiCity y AVIGAE) y un proyecto con financiación en cascada H2020 (Eco-Rural -IoT)

Estrutura de internet das coisas para a participação ativa e segura dos consumidores na comunidade de energia

Desde o Acordo de Paris, diversas estratégias e metas climáticas ambiciosas foram estabelecidas a nível mundial e europeu, de forma a cumprir o objetivo de longo prazo da neutralidade carbónica. Tal, provocou uma pressão a nível global para que se fossem tomadas medidas que contribuíssem para a descarbonização em todos os setores. No entanto, verifica-se que as emissões de CO2 continuam a aumentar, sendo o setor de energia um dos principais responsáveis. Atualmente, no setor de energia, verifica-se a emergência das comunidades de energia e do uso de dispositivos de internet das coisas, visto que estes possibilitam novas oportunidades para integrar, monitorizar, controlar e otimizar o consumo de energia, possibilitando uma melhor eficiência e a sustentabilidade dos sistemas de energia. Contudo, o problema da ausência de confiança digital em sistemas de energia, nomeadamente no que toca à partilha de dados e informação, pode comprometer a sinergia entre o consumidor e os sistemas de gestão de energia. Esta dissertação tem como objetivo conceber, implementar, testar e validar um modelo de demand response que permita gerir eficazmente a participação dos membros de uma comunidade de energia, tendo em consideração a privacidade e a segurança dos dados dos utilizadores finais. Para tal, é considerado o uso de dispositivos de internet das coisas, tendo em conta a segurança e a privacidade dos respetivos dados dos consumidores finais. De uma forma geral, o modelo proposto é capaz de (i) identificar os períodos temporais em que seja benéfica a utilização de demand response para nivelar o consumo e a geração na comunidade de energia, com o auxílio de algoritmos de previsão, (ii) avaliar e classificar os candidatos, através de quatro métricas baseadas em algoritmos não supervisionados, para determinar quais serão selecionados a participar no evento, (iii) monitorizar, em tempo real, o respetivo evento e (iv) avaliar o impacto económico e ambiental que o evento causou na comunidade de energia. De modo a respeitar a privacidade e a equidade dos utilizadores finais, consideram-se três tipos de participação com diferentes níveis de privacidade e um mecanismo de equidade que é implementado durante a classificação dos candidatos. De modo a testar e validar a aplicabilidade e a eficiência do modelo proposto, foram considerados cinco casos de estudo que permitem analisar o desempenho do modelo consoante as métricas aplicadas, os tipos de participação disponíveis, a falta de compromisso dos candidatos para o evento e o mecanismo de equidade. Os resultados obtidos demonstraram a capacidade do modelo proposto ser implementado em diferentes contextos, tendo a capacidade de melhorar a sustentabilidade da comunidade de energia.Since the Paris Agreement, several ambitious climate strategies and targets have been set at global and European levels in order to meet the long-term goal of carbon neutrality. This has led to global pressure for action to contribute to decarbonisation in all sectors. However, CO2 emissions continue to increase, with the energy sector being one of the main contributors. The energy sector is currently experiencing the emergence of energy communities and the use of internet of things devices, as these provide new opportunities to integrate, monitor, control and optimise energy consumption, enabling improved efficiency and sustainability of energy systems. However, the problem of the absence of digital trust in energy systems, namely regarding data and information sharing, can compromise the synergy between the consumer and the energy management systems. This dissertation aims to conceive, implement, test and validate a demand response model that effectively manages the participation of the members of an energy community, considering the privacy and security of the end users' data. To this end, the use of internet of things devices is considered, considering the security and privacy of the respective end-user data. In general, the proposed model can (i) identify the time periods when it is beneficial to use demand response to level consumption and generation in the energy community, with the help of prediction algorithms, (ii) evaluate and classify the candidates through four metrics based on unsupervised algorithms, to determine which ones will be selected to participate in the event, (iii) monitor, in real-time, the respective event and (iv) evaluate the economic and environmental impact that the event caused in the energy community. In order to respect the privacy and fairness of end-users, three types of participation are considered with different levels of privacy and an equity mechanism is implemented during the ranking of candidates. In order to test and validate the applicability and efficiency of the proposed model, five case studies were considered to analyse the model's performance according to the metrics applied, the types of participation available, the lack of commitment of the candidates to the event and the equity mechanism. The results obtained demonstrated the ability of the proposed model to be implemented in different contexts, having the ability to improve the sustainability of the energy community

Feature Papers of Forecasting 2021

This book focuses on fundamental and applied research on forecasting methods and analyses on how forecasting can affect a great number of fields, spanning from Computer Science, Engineering, and Economics and Business to natural sciences. Forecasting applications are increasingly important because they allow for improving decision-making processes by providing useful insights about the future. Scientific research is giving unprecedented attention to forecasting applications, with a continuously growing number of articles about novel forecast approaches being publishe