Search Results (209)

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions. Full article

To address the need for intelligent scheduling and model integration under spatiotemporal variability and uncertainty in water systems, this study proposes a hybrid correction method for pump characteristic curves that integrates data-driven techniques with an affine modeling framework. Steady-state data are extracted through adaptive filtering and statistical testing, and representative operating conditions are identified via unsupervised clustering. An affine transformation is then applied to the factory-provided characteristic equation, followed by parameter optimization using the clustered dataset. Using the Hongze Pump Station along the eastern route of the South-to-North Water Diversion Project as a case study, the method reduced the mean blade angle prediction error from 1.73° to 0.51°, and the efficiency prediction error from 7.32% to 1.30%. The results demonstrate improved model accuracy under real-world conditions and highlight the method’s potential to support more robust and adaptive hydrodynamic scheduling models, contributing to the advancement of sustainable and smart water resource management. Full article

The significant contribution of buildings to the global primary energy consumption necessitates the application of energy management methodologies at a building scale. Although dynamic simulation tools and decision-making algorithms are core components of energy management methodologies, they are often accompanied by excessive computational cost. As future controlling structures tend to become autonomized in building heating layouts, encouraging distributed heating services, the research scope calls for creating lightweight building energy system modeling as well monitoring and controlling methods. Following this notion, the proposed methodology turns a programmable controller into a smart thermostat that utilizes surrogate modeling formed by the ALAMO approach and is applied in a 4-m-by-4-m-by-2.85-m environmental chamber setup heated by a heat pump. The results indicate that the smart thermostat trained on the indoor environmental conditions of the chamber for a one-week period attained a predictive RMSE of 0.082–0.116 °C. Consequently, it preplans the heating hours and applies preheating controlling strategies in real time effectively, using only the computational power of a conventional controller, essentially managing to attain at least 97% thermal comfort on the test days. Finally, the methodology has the potential to meet the requirements of future building energy systems featured in urban-scale RES-based district heating networks. Full article

The transition to low-carbon heating systems is fundamental to achieving climate neutrality, particularly within the building sector, which accounts for a significant share of global greenhouse gas emissions. Among various technologies, heat pumps have emerged as a leading solution due to their high energy efficiency and potential to significantly reduce CO₂ emissions, especially when powered by renewable electricity. This systematic review synthesizes findings from the recent literature, including peer-reviewed studies and industry reports, to evaluate the technical performance, environmental impact, and deployment potential of air source, ground source, and water source heat pumps. This review also investigates life cycle greenhouse gas emissions, the influence of geographical energy mix diversity, and the integration of heat pumps within hybrid and district heating systems. Results indicate that hybrid HP systems achieve the lowest specific GHG emissions (0.108 kgCO₂eq/kWh of heat delivered on average), followed by WSHPs (0.018 to 0.216 kgCO₂eq/kWh), GSHPs (0.050–0.211 kgCO₂eq/kWh), and ASHPs (0.083–0.216 kgCO₂eq/kWh). HP systems show a potential GHG emission reduction of up to 90%, depending on the kind of technology and energy mix. Despite higher investment costs, the lower environmental footprint of GSHPs and WSHPs makes them attractive options for decarbonizing the building sector due to better performance resulting from more stable thermal input and higher SCOP. The integration of heat pumps with thermal storage, renewable energy, and smart control technologies further enhances their efficiency and climate benefits, regardless of the challenges facing their market potential. This review concludes that heat pumps, particularly in hybrid configurations, are a cornerstone technology for sustainable building heat supply and energy transition. Full article

We present a detailed dataset collected via a wireless IoT sensor network monitoring three industrial centrifugal pumps (units A, B, and C) at the Italian Aerospace Research Centre (CIRA), along with the methods for data collection and structuring. Background: Centrifugal pumps are critical in industrial plants, and monitoring their condition is essential to ensure reliability, safety, and efficiency. High-quality operational data under normal operating conditions are fundamental for developing effective maintenance strategies and diagnostic models. Methods: Data were gathered by means of smart sensors measuring motor and pump vibrations, temperatures, outlet fluid pressures, and environmental conditions. Data were transmitted over a WirelessHART mesh network and acquired through an IoT architecture. Results: The dataset consists of eight CSV files, each representing a specific pump during a distinct operational day. Each file includes timestamped measurements of displacement, peak vibration values, sensor temperatures, fluid pressure, ambient temperature, and atmospheric pressure. Conclusions: This dataset supports advanced methodologies in feature extraction, multivariate signal analysis, unsupervised pattern discovery, vibration analysis, and the development of digital twins and soft sensing models for predictive maintenance optimization. Full article

This paper analyzes the process of energy transition from traditional solid fuel heating to an air-to-air (A2A) heat pump-based heating system. Special emphasis was placed on the implementation of new technologies for improved management of energy systems, aiming to elevate both comfort levels and energy efficiency. This paper explores the use of the open-source software Home Assistant as an integration platform for home automation, designed to manage smart home devices while preserving local control, user privacy, and increasing cybersecurity. The proposed hardware platform includes a Raspberry Pi with appropriate IoT modules, providing a flexible and economically viable solution for household needs. Full article

Efficient carbon reduction pathways in the building sector are critical for urban decarbonization. This study predicts urban carbon emissions and establishes models to evaluate the carbon emission reduction potential of applying building low-carbon technologies (LCTs) at the urban scale. The models under consideration encompass a spectrum of active strategies, specifically heat pump (HP), rooftop photovoltaic (PV) systems, and smart heating, ventilation, and air conditioning (HVAC) systems, alongside passive strategies encompassing advanced building materials and building envelopes. The predictive calculations consider building typologies, technological evolution, adoption rates, and local policy constraints. Results indicate that by 2030, the building sector in Xi’an will account for over 30% of the city’s total carbon emissions. The integrated emission reduction effect of LCTs reaches 25.8%, with building materials contributing the most significantly at 9%. Notably, rooftop PV systems demonstrate the highest carbon reduction potential among active strategies, while HP exhibits the fastest annual growth rate in mitigation. Furthermore, the study evaluates the feasibility of these LCTs to accelerate progress toward carbon reduction goals in the building sector. Full article

Air–water heat pumps are gaining interest in modern architectures, and they are a suitable option as a replacement for fossil fuel-based heating systems. These systems consume less electricity by combining solar panels, a heat pump, thermal storage, and a smart control system. This study was applied to a completely ecological rural health sub-center built on the basis of recycled bottles, and that, for its regular operation, requires an energy system according to the needs of the patients in the rural community. Detailed analyses were performed for heating and hot water preparation in two scenarios with different conditions (standard and fully integrated). From a technical perspective, different strategies were analyzed to ensure its functionality. If the photovoltaic system is sized to achieve advanced control, the system can even operate autonomously. However, due to the need to guarantee the energy efficiency of the center, the analyses were performed with a grid connection, and it was determined that the photovoltaic system guarantees at least two-thirds of the energy required for its autonomous operation. The results show that the system can operate normally thanks to the optimal size of the photovoltaic system, which positively influences the rural population in the case under analysis. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. Full article

Background/Objectives: Potassium-competitive acid blockers (P-CABs) offer rapid gastric acid inhibition and lower toxicity compared to proton pump inhibitors (PPIs). This study investigates the drug–drug interaction between P-CABs and immunosuppressants tacrolimus and mycophenolate in kidney transplant recipients (KTRs). Methods: Sixty-two KTRs were randomized to receive either 50 mg of tegoprazan or 20 mg of pantoprazole. Patients were monitored using a smart clinical trial platform incorporating remote monitoring and safety management systems, which tracked drug adherence and vital signs. General and gastrointestinal (GI) symptoms were surveyed via a self-developed app on patients’ phones. Trough levels of tacrolimus and mycophenolate were measured every 4 weeks over 12 weeks. Results: Medication adherence was 100% in both groups. A total of 13,726 biometric data points and 5031 questionnaire responses were collected, with 5704 feedback messages and 56 video visits conducted. At 12 weeks, the mean trough levels of tacrolimus and mycophenolate were similar between the tegoprazan and pantoprazole groups (5.5 ± 1.6 vs. 5.8 ± 2.0 ng/mL, p = 0.50 and 2.7 ± 1.4 vs. 2.6 ± 1.4 µg/mL, p = 0.57, respectively). The intragroup difference in trough levels from baseline to week 12 was not significant in either group. GI symptoms scores, vital signs, and allograft function remained stable and comparable between groups. Conclusions: Tegoprazan does not alter the blood trough levels of tacrolimus and mycophenolate during the 12-week follow-up in KTRs and has a similar impact on GI symptoms as pantoprazole. This study confirms the feasibility and safety of using a smart clinical trial system with remote monitoring for randomized trials. Full article

The integration of new technologies in Industry 4.0 has modernised agriculture, fostering the concept of smart agriculture (Agriculture 4.0). Higher education institutions are incorporating digital technologies into agricultural curricula, equipping students in agriculture, agronomy, and engineering with essential skills. The implementation of targeted STEM activities has the potential to enhance the teaching of Agriculture 4.0 through the utilisation of practical applications that stimulate student interest, thereby facilitating more accessible and effective teaching. In this context, this study presents a system comprising retrofitted real-scale components that facilitate the understanding of digital technologies and automations in agriculture. The specific system utilises a typical centrifugal electric pump and a water tank and adds logic to it, so that its flow follows various user-defined setpoints, given and monitored via a smartphone application, despite the in-purpose disturbances invoked via intermediating valves. This setup aims for students to gain familiarity with concepts such as closed-loop systems and PID controllers. Going further, fertile ground is provided for experimentation on the efficiency of the PID controller via testing different algorithmic variants incorporating non-linear methods as well. Feedback collected from the participating students via a corresponding survey highlights the importance of integrating similar hands-on interdisciplinary activities into university curricula to foster engineering education. Full article

In this work, the seasonal performance of a dual-source heat pump (DSHP) prototype, able to exploit aerothermal and geothermal energy, was assessed experimentally. The unit, operated under the working conditions of two representative heating days (RDs), was coupled to a real undersized borehole heat exchanger (BHE) field. A distributed temperature sensing (DTS) system, installed in the borefield, was adopted to monitor the ground thermal response during the DSHP operation. In order to compare the DSHP performance to that of a traditional air-source heat pump (ASHP), the same RDs were reproduced in the test rig operating the DSHP in air mode only, and then exploiting both heat sources. Comparing the efficiency of the DSHP and ASHP, it is noticed that the additional exploitation of geothermal energy can increase system efficiency by up to 3% on a seasonal basis. Indeed, the DSHP coupled to an undersized BHE can operate in ground mode until it is energy-efficient; then, the required building load is supplied by exploiting the aerothermal energy source. In this way, the BHE investment cost can be reduced, and the ground temperature drift originating from unbalanced building loads can be limited through the smart exploitation of both sources. Full article

The electrification of energy systems is essential for carbon reduction and sustainable energy goals. However, current network asset ratings and the poor thermal efficiency of older buildings pose significant challenges. This study evaluates the impact of heat pump and electric vehicle (EV) penetration on a UK residential distribution network, considering the highest coincident electricity demand and worst weather conditions recorded over the past decade. The power flow calculation, based on Python, is performed using the pandapower library, leveraging the actual distribution network structure of the Hillingdon area by incorporating recent smart meter data from a distribution system operator alongside historical weather data from the past decade. Based on the outcome of power flow calculation, the transformer loadings and voltage levels were assessed for existing and projected heat pump and EV adoption rates, in line with national policy targets. Findings highlight that varied consumer density and diverse usage patterns significantly influence upgrade requirements. Full article

This study addresses the growing need for predictive maintenance in the maritime industry by proposing an optimized strategy for ship engine maintenance. The aim is to reduce unplanned failures that cause significant financial losses and disrupt global logistics flows. The methodology integrates Weibull reliability analysis, Markov chains, and Data Envelopment Analysis (DEA). A dataset of 512 diesel engine components from container ships was analysed, where the Weibull distribution (β = 1.8; α = 18,500 h) accurately modelled failure patterns, and Markov chains captured transitions between operational states (normal, degraded, failure). DEA was used to evaluate the efficiency of different maintenance strategies. Results indicate that targeting interventions in the degraded state significantly reduces downtime and improves component reliability, particularly for high-pressure fuel pumps and turbochargers. Optimizing maintenance extended the Mean Time to Failure (MTTF) up to 22,000 h and reduced the proportion of failures in critical components from 64.3% to 40%. These findings support a transition from reactive to proactive maintenance models, contributing to enhanced fleet availability, safety, and cost-effectiveness. The approach provides a quantitative foundation for predictive maintenance planning, with potential application in fleet management systems and smart ship platforms. Full article

Heat pumps (HPs) are considered as a key technology in the future energy system. Besides technical and ecological aspects, user acceptance and user friendliness are also essential. The aim of the study was therefore to research which aspects are decisive for the purchase decision, which different types of HP owners can be distinguished, how their specific user behavior can be characterized in terms of control and operation, and what their respective requirements and wishes are for the functions and operation of their HPs. A mixed-methods approach in an exploratory sequential design was used. Based on nine qualitative interviews and a survey with 510 respondents, both conducted in Austria, it is observed that the most relevant arguments for the purchase decision of HPs are high environmental friendliness and efficiency, as well as resource independence. Respecting certain usage and requirement patterns, four user types could be identified and defined—the minimalist, the functionalist, the tech-savvy tinkerer, and the anxious user. In the future, intelligent control and regulation approaches and the integration of HPs into a holistic energy and building management system (smart home) will become more important. Based on the results, tailor-made system solutions can be developed, user friendliness optimized, and new services developed. Full article