Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl

Scaling energy management in buildings with artificial intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning

Artificial Intelligence-based Smarter Accessibility Evaluations for Comprehensive and Personalized Assessment

The research focuses on utilizing artificial intelligence (AI) and machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) in three areas: public buildings, homes, and medical devices. The overarching goal is to improve the accuracy, reliability, and effectiveness of accessibility evaluation systems by leveraging smarter technologies. For public buildings, the challenge lies in developing an accurate and reliable accessibility evaluation system. AI can play a crucial role by analyzing data, identifying potential barriers, and assessing the accessibility of various features within buildings. By training ML algorithms on relevant data, the system can learn to make accurate predictions about the accessibility of different spaces and help policymakers and architects design more inclusive environments. For private places such as homes, it is essential to have a person-focused accessibility evaluation system. By utilizing machine learning-based intelligent systems, it becomes possible to assess the accessibility of individual homes based on specific needs and requirements. This personalized approach can help identify barriers and recommend modifications or assistive technologies that can enhance accessibility and independence for PwD within their own living spaces. The research also addresses the intelligent evaluation of healthcare devices in the home. Many PwD rely on medical devices for their daily living, and ensuring the accessibility and usability of these devices is crucial. AI can be employed to evaluate the accessibility features of medical devices, provide recommendations for improvement, and even measure their effectiveness in supporting the needs of PwD. Overall, this research aims to enhance the accuracy and reliability of accessibility evaluation systems by leveraging AI and ML technologies. By doing so, it seeks to improve the quality of life for individuals with disabilities by enabling increased independence, fostering social inclusion, and promoting better accessibility in public buildings, private homes, and medical devices

DevOps for Trustworthy Smart IoT Systems

ENACT is a research project funded by the European Commission under its H2020 program. The project consortium consists of twelve industry and research member organisations spread across the whole EU. The overall goal of the ENACT project was to provide a novel set of solutions to enable DevOps in the realm of trustworthy Smart IoT Systems. Smart IoT Systems (SIS) are complex systems involving not only sensors but also actuators with control loops distributed all across the IoT, Edge and Cloud infrastructure. Since smart IoT systems typically operate in a changing and often unpredictable environment, the ability of these systems to continuously evolve and adapt to their new environment is decisive to ensure and increase their trustworthiness, quality and user experience. DevOps has established itself as a software development life-cycle model that encourages developers to continuously bring new features to the system under operation without sacrificing quality. This book reports on the ENACT work to empower the development and operation as well as the continuous and agile evolution of SIS, which is necessary to adapt the system to changes in its environment, such as newly appearing trustworthiness threats

Deployment of DeepTech AI Models in Engineering Solutions

Ponencia presentada en ICRAMAE-2k21, International Conference on Recent Advances in Mechanical and Automation Engineering, Vivekananda Global University, Jaipur, India, 29-30th November 2021[EN]Industrial Engineering is a branch of engineering that focuses on the design and operation of industrial processes. It involves the application of science to the construction of production systems. This field has undergone significant advancements over the last decades. In the last centuries, the emergence of different technologies has led to breakthroughs in engineering, making it possible to automate processes in industries. Steam, electricity, the internet, and now Artificial Intelligence technologies have all brought with them greater levels of automation to machinery, gradually decreasing human involvement in processes such as procurement, raw material handling, manufacturing and quality control