Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Uplink data measurement and analysis for 5G eCPRI radio unit

Abstract. The new 5G mobile network generation aims to enhance the performance of the cellular network in almost every possible aspect, offering higher data rates, lower latencies, and massive number of network connections. Arguably the most important change from LTE are the new RU-BBU split options for 5G promoted by 3GPP and other organizations. Another big conceptual shift introduced with 5G is the open RAN concept, pushed forward by organizations such as the O-RAN alliance. O-RAN aims to standardize the interfaces between different RAN elements in a way that promotes vendor interoperability and lowers the entry barrier for new equipment suppliers. Moreover, the 7-2x split option standardized by O-RAN has risen as the most important option within the different low layer split options. As the fronthaul interface, O-RAN has selected the packet-based eCPRI protocol, which has been designed to be more flexible and dynamic in terms of transport network and data-rates compared to its predecessor CPRI. Due to being a new interface, tools to analyse data from this interface are lacking. In this thesis, a new, Python-based data analysis tool for UL eCPRI data was created for data quality validation purposes from any O-RAN 7-2x functional split based 5G eCPRI radio unit. The main goal for this was to provide concrete KPIs from captured data, including timing offset, signal power level and error vector magnitude. The tool produces visual and text-based outputs that can be used in both manual and automated testing. The tool has enhanced eCPRI UL datapath testing in radio unit integration teams by providing actual quality metrics and enabling test automation.Uplink datamittaukset ja -analyysi 5G eCPRI radiolla. Tiivistelmä. Uusi 5G mobiiliverkkogeneraatio tuo mukanaan parannuksia lähes kaikkiin mobiiliverkon ominaisuuksiin, tarjoten nopeamman datasiirron, pienemmät viiveet ja valtavat laiteverkostot. Luultavasti tärkein muutos LTE teknologiasta ovat 3GPP:n ja muiden organisaatioiden ehdottamat uudet radion ja systeemimoduulin väliset funktionaaliset jakovaihtoehdot. Toinen huomattava muutos 5G:ssä on O-RAN:in ajama avoimen RAN:in konsepti, jonka tarkoituksena on standardisoida verkkolaitteiden väliset rajapinnat niin, että RAN voidaan rakentaa eri valmistajien laitteista, laskien uusien laitevalmistajien kynnystä astua verkkolaitemarkkinoille. O-RAN:n standardisoima 7-2x funktionaalinen jako on noussut tärkeimmäksi alemman tason jakovaihtoehdoista. Fronthaul rajapinnan protokollaksi O-RAN on valinnut pakettitiedonsiirtoon perustuvan eCPRI:n, joka on suunniteltu dynaamisemmaksi ja joustavammaksi datanopeuksien ja lähetysverkon suhteen kuin edeltävä CPRI protokolla. Uutena protokollana, eCPRI rajapinnalle soveltuvia data-analyysityökaluja ei ole juurikaan saatavilla. Tässä työssä luotiin uusi pythonpohjainen data-analyysityökalu UL suunnan eCPRI datalle, jotta datan laatu voidaan määrittää millä tahansa O-RAN 7-2x funktionaaliseen jakoon perustuvalla 5G eCPRI radiolla. Työkalun päätarkoitus on analysoida ja kuvata datan laatua laskemalla datan ajoitusoffsettia, tehotasoa, sekä EVM:ää. Työkalu tuottaa tulokset visuaalisena ja tekstipohjaisena, jotta analyysia voidaan tehdä niin manuaalisessa kuin automaattisessa testauksessa. Työkalun käyttöönotto on tehostanut UL suunnan dataputken testausta radio-integrointitiimeissä, tarjoten datan laatua kuvaavaa metriikkaa sekä mahdollistaen testauksen automatisoinnin

Road2CPS priorities and recommendations for research and innovation in cyber-physical systems

This document summarises the findings of the Road2CPS project, co-financed by the European Commission under the H2020 Research and Innovation Programme, to develop a roadmap and recommendations for strategic action required for future deployment of Cyber-Physical Systems (CPS). The term Cyber-Physical System describes hardware-software systems, which tightly couple the physical world and the virtual world. They are established from networked embedded systems that are connected with the outside world through sensors and actuators and have the capability to collaborate, adapt, and evolve. In the ARTEMIS Strategic Research Agenda 2016, CPS are described as ‘Embedded Intelligent ICT Systems’ that make products smarter, more interconnected, interdependent, collaborative, and autonomous. In the future world of CPS, a huge number of devices connected to the physical world will be able to exchange data with each other, access web services, and interact with people. Moreover, information systems will sense, monitor and even control the physical world via Cyber-Physical Systems and the Internet of Things (HiPEAC Vision 2015). Cyber-Physical Systems find their application in many highly relevant areas to our society: multi-modal transport, health, smart factories, smart grids and smart cities amongst others. The deployment of Cyber-Physical Systems (CPS) is expected to increase substantially over the next decades, holding great potential for novel applications and innovative product development. Digital technologies have already pervaded day-to-day life massively, affecting all kinds of interactions between humans and their environment. However, the inherent complexity of CPSs, as well as the need to meet optimised performance and comply with essential requirements like safety, privacy, security, raises many questions that are currently being explored by the research community. Road2CPS aims at accelerating uptake and implementation of these efforts. The Road2CPS project identifying and analysing the relevant technology fields and related research priorities to fuel the development of trustworthy CPS, as well as the specific technologies, needs and barriers for a successful implementation in different application domains and to derive recommendations for strategic action. The document at hand was established through an interactive, community-based approach, involving over 300 experts from academia, industry and policy making through a series of workshops and consultations. Visions and priorities of recently produced roadmaps in the area of CPS, IoT (Internet of Things), SoS (System-of-Systems) and FoF (Factories of the Future) were discussed, complemented by sharing views and perspectives on CPS implementation in application domains, evolving multi-sided eco-systems as well as business and policy related barriers, enablers and success factors. From the workshops and accompanying activities recommendations for future research and innovation activities were derived and topics and timelines for their implementation proposed. Amongst the technological topics, and related future research priorities ‘integration, interoperability, standards’ ranged highest in all workshops. The topic is connected to digital platforms and reference architectures, which have already become a key priority theme for the EC and their Digitisation Strategy as well as the work on the right standards to help successful implementation of CPSs. Other themes of very high technology/research relevance revealed to be ‘modelling and simulation’, ‘safety and dependability’, ‘security and privacy’, ‘big data and real-time analysis’, ‘ubiquitous autonomy and forecasting’ as well as ‘HMI/human machine awareness’. Next to this, themes emerged including ‘decision making and support’, ‘CPS engineering (requirements, design)’, ‘CPS life-cycle management’, ‘System-of-Systems’, ‘distributed management’, ‘cognitive CPS’, ‘emergence, complexity, adaptability and flexibility’ and work on the foundations of CPS and ‘cross-disciplinary research/CPS Science’

Digitalization of Offshore Wind Farm Systems

Master's thesis in Offshore Technology: Industrial asset managementThis thesis investigates how new digital technologies and digitalization can help further evolve the offshore wind industry using the Industry 4.0 concept as a basis and explores how technologies within this concept can contribute to an offshore wind farm that overcomes some of these challenges. The study focuses on an offshore wind farm from a systems perspective, including respective modules, and where the Industry 4.0 technologies can be applied. Following this is the establishment of a systematic digitalization framework and a proposal on how to cope with increased volumes of data, connectivity, and complexity.publishedVersio

Fuzzy logic-based vehicle safety estimation using V2V communications and on-board embedded ROS-based architecture for safe traffic management system in hail city

Estimating the state of surrounding vehicles is crucial to either prevent or avoid collisions with other road users. However, due to insufficient historical data and the unpredictability of future driving tactics, estimating the safety status is a difficult undertaking. To address this problem, an intelligent and autonomous traffic management system based on V2V technology is proposed. The main contribution of this work is to design a new system that uses a real-time control system and a fuzzy logic algorithm to estimate safety. The robot operating system (ROS) is the foundation of the control architechture, which connects all the various system nodes and generates the decision in the form of a speech and graphical message. The safe path is determined by a safety evaluation system that combines sensor data with a fuzzy classifier. Moreover, the suitable information processed by each vehicle unit is shared in the group to avoid unexpected problems related to speed, sudden braking, unplanned deviation, street holes, road bumps, and any kind of street issues. The connection is provided through a network based on the ZigBee protocol. The results of vehicle tests show that the proposed method provides a more reliable estimate of safety as compared to other methods

Multi-domain Software Defined Networking: Research status and challenges

A key focus of the transition to next generation computer networking is to improve management of network services thereby enhancing traffic control and flows while simplifying higher-level functionality. Software-defined networking (SDN) is an approach that is being developed to facilitate next generation computer networking by decoupling the traffic control system from the underlying traffic transmission system. SDN offers programmability in network services by separating the control plane from the data plane within network devices and providing programmability for network services. Enhanced connectivity services across the global digital network require a multi-domain capability. This paper presents a review of the current research status in SDN and multi-domain SDN, focusing on OpenFlow protocol, and its future related challenges

Real-Time intelligence

Dissertação de mestrado em Computer ScienceOver the past 20 years, data has increased in a large scale in various fields. This explosive increase of global data led to the coin of the term Big Data. Big data is mainly used to describe enormous datasets that typically includes masses of unstructured data that may need real-time analysis. This paradigm brings important challenges on tasks like data acquisition, storage and analysis. The ability to perform these tasks efficiently got the attention of researchers as it brings a lot of oportunities for creating new value. Another topic with growing importance is the usage of biometrics, that have been used in a wide set of application areas as, for example, healthcare and security. In this work it is intended to handle the data pipeline of data generated by a large scale biometrics application providing basis for real-time analytics and behavioural classification. The challenges regarding analytical queries (with real-time requirements, due to the need of monitoring the metrics/behavior) and classifiers’ training are particularly addressed.Nos os últimos 20 anos, a quantidade de dados armazenados e passíveis de serem processados, tem vindo a aumentar em áreas bastante diversas. Este aumento explosivo, aliado às potencialidades que surgem como consequência do mesmo, levou ao aparecimento do termo Big Data. Big Data abrange essencialmente grandes volumes de dados, possivelmente com pouca estrutura e com necessidade de processamento em tempo real. As especificidades apresentadas levaram ao aparecimento de desafios nas diversas tarefas do pipeline típico de processamento de dados como, por exemplo, a aquisição, armazenamento e a análise. A capacidade de realizar estas tarefas de uma forma eficiente tem sido alvo de estudo tanto pela indústria como pela comunidade académica, abrindo portas para a criação de valor. Uma outra área onde a evolução tem sido notória é a utilização de biométricas comportamentais que tem vindo a ser cada vez mais acentuada em diferentes cenários como, por exemplo, na área dos cuidados de saúde ou na segurança. Neste trabalho um dos objetivos passa pela gestão do pipeline de processamento de dados de uma aplicação de larga escala, na área das biométricas comportamentais, de forma a possibilitar a obtenção de métricas em tempo real sobre os dados (viabilizando a sua monitorização) e a classificação automática de registos sobre fadiga na interação homem-máquina (em larga escala)