Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of ...extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.
Smart buildings are rapidly becoming more prevalent, aiming to create energy-efficient and comfortable living spaces. Nevertheless, the design of a smart building is a multifaceted approach that ...faces numerous challenges, with the primary one being the algorithm needed for energy management. In this paper, the design of a smart building, with a particular emphasis on the algorithm for controlling the indoor environment, is addressed. The implementation and evaluation of the Advantage-Weighted Actor-Critic algorithm is examined in a four-unit residential simulated building. Moreover, a novel self-adapted Advantage-Weighted Actor-Critic algorithm is proposed, tested, and evaluated in both the simulated and real building. The results underscore the effectiveness of the proposed control strategy compared to Rule-Based Controllers, Deep Deterministic Policy Gradient, and Advantage-Weighted Actor-Critic. Experimental results demonstrate a 34.91% improvement compared to the Deep Deterministic Policy Gradient and a 2.50% increase compared to the best Advantage-Weighted Actor-Critic method in the first epoch during a real-life scenario. These findings solidify the Self-Adapted Advantage-Weighted Actor-Critic algorithm’s efficacy, positioning it as a promising and advanced solution in the realm of smart building optimization.
THe energy-efficient operation of microgrids—a localized grouping of consuming loads (domestic appliances, EVs, etc.) with distributed energy sources such as solar photovoltaic panels—suggests the ...deployment of Energy Management Systems (EMSs) that enable the actuation of controllable microgrid loads coupled with Artificial Intelligence (AI) tools. Such tools are capable of optimizing the aggregated performance of the microgrid in an automated manner, based on an extensive network of Advanced Metering Infrastructure (AMI). Modular adaptable/dynamic building envelope (ADBE) solutions have been proven an effective solution—exploiting free façade areas instead of roof areas—for extending the thermal inertia and energy harvesting capacity in existing buildings of different nature (residential, commercial, industrial, etc.). This study presents the PLUG-N-HARVEST holistic workflow towards the delivery of an automatically controllable microgrid integrating active ADBE technologies (e.g., PVs, HVACs). The digital platform comprises cloud AI services and functionalities for energy-efficient management, data healing/cleansing, flexibility forecasting, and the security-by-design IoT to efficiently optimize the overall performance in near-zero energy buildings and microgrids. The current study presents the effective design and necessary digital integration steps towards the PLUG-N-HARVEST ICT platform alongside real-life verification test results, validating the performance of the platform.
The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an ...advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system’s real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments.
Though much attention is dedicated to the development of its research and innovation policy, the European Union constantly struggles to match the level of the strongest innovators in the world. ...Therefore, there is a necessity to analyze the individual efforts and conditions of the 27 member states that might determine their final innovative performance. The results of a scientific literature review showed that there is a growing interest in the usage of artificial intelligence when seeking to improve decision-making processes. Data envelopment analysis, as a branch of computational intelligence methods, has proved to be a reliable tool for innovation efficiency evaluation. Therefore, this paper aimed to apply DEA for the assessment of the European Union’s innovation efficiency from 2000 to 2020, when innovation was measured by patent, trademark, and design applications. The findings showed that the general EU innovation efficiency situation has improved over time, meaning that each programming period was more successful than the previous one. On the other hand, visible disparities were found across the member states, showing that Luxembourg is an absolute innovation efficiency leader, while Greece and Portugal achieved the lowest average efficiency scores. Both the application of the DEA method and the gathered results may act as viable guidelines on how to improve R&I policies and select future investment directions.
The field of archaeological excavation has seen enormous developments as a result of the quick development of digital technologies. It is critical to acknowledge the long-term benefits of new ...approaches. In this regard, this study intends to suggest a system that provides archaeologists with digital tools that allow them to fully and effectively document their excavations in real time while in the field, which is specifically designed for classical and Byzantine archaeology. The system not only facilitates traditional documentation practices but also integrates advanced geospatial technologies and augmented reality, enhancing the accuracy and depth of archaeological research and preservation. This dual functionality enables both the efficient recording of excavation data as experienced by excavators on-site and the detailed documentation processed by researchers post-excavation. The objective of the application is to generate a sophisticated system that will enable the excavation data and experience that the excavator gains on the site of work to be recorded in real time, as well as the documentation that is subsequently processed and accomplished by the researcher or by other researchers. The system saves detailed images and 3D models of artifacts and excavation sites. This ensures that every detail is recorded while preserving the data for future analysis and reference. Lastly, the application was tested in realistic use case scenarios and real-world settings, which increased the system’s credibility and demonstrated its capability to enhance the procedures involved in archaeological excavation and documentation.
The accurate assessment of visual comfort in indoor spaces is crucial for creating environments that enhance occupant well-being, productivity, and overall satisfaction. This paper presents a ...groundbreaking contribution to the field of visual comfort assessment in occupied buildings, addressing the existing research gap in methods for evaluating visual comfort once a building is in use while ensuring compliance with design specifications. The primary aim of this study was to introduce a pioneering approach for estimating visual comfort in indoor environments that is non-intrusive, practical, and can deliver accurate results without compromising accuracy. By incorporating mathematical visual comfort estimation into a regression model, the proposed method was evaluated and compared using real-life scenario. The experimental results demonstrated that the suggested model surpassed the mathematical model with an impressive performance improvement of 99%, requiring fewer computational resources and exhibiting a remarkable 95% faster processing time.
Autonomous PV systems offer a substitute for or a safety net against the transmission and distribution companies’ rising and expanding electricity delivery fees. Across the world, delivery fees have ...climbed by about 3% annually while the cost of energy supply has only gone up by 5% over the past 15 years. In this paper, the drawbacks of PV system integration are highlighted while introducing a unique autonomous PV system. The system is also constructed in a reproducible way so that it can be duplicated. Replicability is showcased by the presentation of two separate PV systems that were created and evaluated using the same methodology. One system uses Si-based conventional photovoltaics, while the other uses dye-sensitized solar cells. The systems that are being offered also support remote connections, allowing all of the data from the PV system to be transmitted to a single endpoint (e.g., a visualization platform). Moreover, the system’s batteries were modified to accommodate the two PV systems. Finally, experimental results showcase the operability of the proposed architecture.
Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques ...must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns.
This concept paper presents our viewpoint regarding the exploitation of cutting-edge technologies for the delivery of smart tourism cultural tours. Specifically, the paper reports preliminary work on ...the design of a novel smart tourism solution tailored to a multiobjective optimization system based on factors such as the preferences and constraints of the tourist/visitor, the city’s accessibility and traffic, the weather conditions, and others. By optimizing cultural tours and delivering comfortable, easy-to-follow, green, acceptable visiting experiences, the proposed solution, namely, OptiTours, aims to become a leading actor in tourism industry transformation. Moreover, specific actions, applications, and methodologies target increasing touring acceptance while advancing the overall (smart) city impression. OptiTours aims to deliver a novel system to attract visitors and guide them to enjoy a city’s possible points of interest, achieving high visitor acceptance. Advanced technologies in semantic trajectories’ management and optimization in route planning will be exploited towards the discovery of optimal, smart, green, and comfortable routes/tours. A novel multiscale and multifactor optimization system aims to deliver not only optimal personalized routes but also alternative routes, ranked based on visitors’ preferences and constraints. In this concept paper, we contribute a detailed description of the OptiTours approach for ICT-based smart tourism, and a high-level architectural design of the solution that is planned to be implemented in the near future.
