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The increasing need for energy efficiency and sustainability as made reducing energy consumption in buildings a top priority. Information and communication technologies (ICT) have been proven to be an effective tool in achieving this goal. However, many legacy appliances in commercial and residential buildings are energy-intensive and not connected, making their replacement with new, more efficient models a significant upfront cost. In this paper, a solution is presented that integrates legacy equipment such as heating, ventilation, and air conditioning (HVAC) systems and other services into a new internet of things (IoT) platform that can integrate machine learning (ML) algorithms to identify the most effective way of achieving a variety of low-energy goals. Through this platform, features such as flexibility services are enabled, allowing energy cost reduction and other beneficial results. The solution has been tested in real demonstration sites in Ireland, and Greece, considering the specific challenges in each country. The Irish demonstration sites showcased the ability to manage a water heater in a residential building and a CHP unit in a commercial one, resulting in energy consumption reductions of up to 39% and 61%, respectively. In the Greek demonstration sites, the implemented solution in the residential building was able to reduce energy consumption by up to 86% during peak hours, and up to 60% overall, offering a 10% reduction in their monthly bill. In the commercial building the services provided an average of 6.9 kWh (or 4.6%) of energy savings per day leading to a 22% reduction in their monthly electricity bill.

The building sector is responsible for a significant amount of energy consumption and greenhouse gas (GHG) emissions. Thus, the monitoring, control and optimization of energy consumption in buildings will play a critical role in the coming years in improving energy efficiency in the building sector and in reducing greenhouse gas emissions. However, while there are a significant number of studies on how to make buildings smarter and manage energy through smart devices, there is a need for more research on integrating buildings with legacy equipment and systems. It is therefore vital to define mechanisms to improve the use of energy efficiency in existing buildings. This study proposes a new architecture (PHOENIX architecture) for integrating legacy building systems into scalable energy management systems with focus also on user comfort in the concept of interoperability layers. This interoperable and intelligent architecture relies on Artificial Intelligence/Machine Learning (AI/ML) and Internet of Things (IoT) technologies to increase building efficiency, grid flexibility and occupant well-being. To validate the architecture and demonstrate the impact and replication potential of the proposed solution, five demonstration pilots have been utilized across Europe. As a result, by implementing the proposed architecture in the pilot sites, 30 apartments and four commercial buildings with more than 400 devices have been integrated into the architecture and have been communicating successfully. In addition, six Trials were performed in a commercial building and five key performance indicators (KPIs) were measured in order to evaluate the robust operation of the architecture. Work is still ongoing for the trials and the KPIs’ analysis after the implementation of PHOENIX architecture at the rest of the pilot sites.

Mitigating design flaws often provides the only means to protect legacy equipment, particularly in wireless local area networks. A synchronous active attack against the wired equivalent privacy protocol demonstrates how mitigating one flaw or attack can facilitate another. [ABSTRACT FROM PUBLISHER]

The transition towards smart manufacturing is challenging for companies with a considerable installed base of legacy machines and equipment. In this regard, smart retrofitting has been introduced as a sustainable approach of transforming the current state of legacy equipment into smart and connected assets. By equipping the existing installed base with hardware and software as well as networking capability, smart retrofitting allows for new data-driven processes and business models. While we see a growing number of articles that focus on retrofitting in the context of smart manufacturing, there is still a lack of a uniform definition of smart retrofitting as well as a holistic understanding of its drivers, challenges, and benefits. Therefore, we conducted a systematic review of the retrofitting literature available through the online databases Scopus and Web of Science. We identified 23 particularly relevant academic articles for a detailed full-text analysis. In this full-text analysis, we searched for definitions of smart retrofitting and synthesized them into a novel definition. Furthermore, we inductively derived contextual drivers and challenges of smart retrofitting as well as retrofit benefits. This literature review provides an overview of the current body of knowledge on smart retrofitting. For academics, our findings show that current research on smart retrofitting focuses on new technologies and legacy systems but largely disregards transformational aspects regarding business models and servitization as well as sustainability aspects, although these deserve more attention in times of global climate challenges. For practitioners, we provide an overview of the drivers, challenges, and benefits that can guide their smart retrofitting initiatives. [Display omitted] [ABSTRACT FROM AUTHOR]