Search Results (581)

The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R² of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R² of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article

The growing global demand for electricity, driven by the development of electromobility, data centers, and smart technologies, necessitates innovative approaches to energy generation. Wind power, as a clean and renewable energy source, plays a pivotal role in the global transition towards low-carbon power systems. This paper presents a comprehensive review of generator technologies used in wind turbine applications, ranging from conventional synchronous and asynchronous machines to advanced concepts such as low-speed direct-drive (DD) generators, axial-flux topologies, and superconducting generators utilizing low-temperature superconductors (LTS) and high-temperature superconductors (HTS). The advantages and limitations of each design are discussed in the context of efficiency, weight, reliability, scalability, and suitability for offshore deployment. Special attention is given to HTS-based generator systems, which offer superior power density and reduced losses, along with challenges related to cryogenic cooling and materials engineering. Furthermore, the paper analyzes selected modern generator designs to provide references for enhancing the performance of grid-synchronized hybrid microgrids integrating solar PV, wind, battery energy storage, and HTS-enhanced generators. This review serves as a valuable resource for researchers and engineers developing next-generation wind energy technologies with improved efficiency and integration potential. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

The growing number of daylighting metrics—often overlapping in scope or terminology—combined with the need for prior familiarization to interpret and apply them effectively, has created a barrier to their adoption beyond academic settings. Consequently, this study analyzes a representative set of established and emerging daylighting metrics to evaluate applicability, synergies, and limitations. Particular attention is given to their implications for occupant health, well-being, performance, and energy use, especially within the context of sensorless smart control systems. A virtual room model was simulated using DaySim 3.1 in two contrasting climates—Seville and London—with varying window-to-wall ratios, orientations, and occupancy schedules. The results show that no single metric provides a comprehensive daylighting assessment, highlighting the need for combined approaches. Daylighting Autonomy (DA) proved useful for task illumination, while Useful Daylight Illuminance (UDI) helped identify areas prone to excessive solar exposure. Spatial metrics such as Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE) offer an overview but lack necessary granularity. Circadian Stimulus Autonomy (CSA) appears promising for evaluating circadian entrainment, though its underlying models remain under refinement. Continuous Overcast Daylight Autonomy (DAo.con) shows the potential for sensorless lighting control when adjusted for orientation. A nuanced, multi-metric approach is therefore recommended. Full article

Accurate forecasting of electricity production is crucial for the stability of the entire energy sector. However, predicting future renewable energy production and its value is difficult due to the complex processes that affect production using renewable energy sources. In this article, we examine the performance of basic deep learning models for electricity forecasting. We designed deep learning models, including recursive neural networks (RNNs), which are mainly based on long short-term memory (LSTM) networks; gated recurrent units (GRUs), convolutional neural networks (CNNs), temporal fusion transforms (TFTs), and combined architectures. In order to achieve this goal, we have created our benchmarks and used tools that automatically select network architectures and parameters. Data were obtained as part of the NCBR grant (the National Center for Research and Development, Poland). These data contain daily records of all the recorded parameters from individual solar and wind farms over the past three years. The experimental results indicate that the LSTM models significantly outperformed the other models in terms of forecasting. In this paper, multilayer deep neural network (DNN) architectures are described, and the results are provided for all the methods. This publication is based on the results obtained within the framework of the research and development project “POIR.01.01.01-00-0506/21”, realized in the years 2022–2023. The project was co-financed by the European Union under the Smart Growth Operational Programme 2014–2020. Full article

Cruciferous vegetables are popular in Asian dishes. However, striped flea beetles prefer to feed on leaves, which can damage the appearance of crops and reduce their economic value. Due to the lack of pest monitoring, the occurrence of pests is often irregular and unpredictable. Regular and quantitative spraying of pesticides for pest control is an alternative method. Nevertheless, this requires manual execution and is inefficient. This paper presents a system powered by solar energy, utilizing batteries and supercapacitors for energy storage to support the implementation of edge AI devices in outdoor environments. Raspberry Pi is utilized for artificial intelligence image recognition and the Internet of Things (IoT). YOLOv5 is implemented on the edge device, Raspberry Pi, for detecting striped flea beetles, and StyleGAN3 is also utilized for data augmentation in the proposed system. The recognition accuracy reaches 85.4%, and the results are transmitted to the server through a 4G network. The experimental results indicate that the system can operate effectively for an extended period. This system enhances sustainability and reliability and greatly improves the practicality of deploying smart pest detection technology in remote or resource-limited agricultural areas. In subsequent applications, drones can plan routes for pesticide spraying based on the distribution of pests. Full article

Phase-change microcapsules offer significant advantages for thermal energy storage and regulation. However, conventional mechanical agitation fabrication methods encounter difficulties in achieving monodispersity, precise size control, and structural uniformity. Droplet microfluidics emerges as a promising alternative, enabling controllable production of microcapsules with tunable sizes (1–1000 μm), programmable core–shell configurations, and high encapsulation efficiency. This review comprehensively summarizes recent advances in microfluidic strategies for phase-change microcapsules fabricating, including single encapsulation, multi-core encapsulation, and high-throughput parallelization and their applications in solar energy storage, building thermal regulation, electronics cooling, and smart textiles. The review highlights key challenges for future advancement which will unlock the full potential of microfluidics-engineered phase-change microcapsules in next-generation thermal energy technologies. Full article

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

Tungsten-doped vanadium dioxide (W-VO₂) shows semiconductor-to-metal phase transition properties at room temperature, which is an ideal thermo-chromic smart window material. However, low visual transmittance and solar modulation limit its application in building energy saving. In this paper, a W-VO_2−x@AA core-shell nanoparticle is proposed to improve the thermo-chromic performance of W-VO₂. Oxygen vacancies were used to promote the connection of W-VO_2−x nanoparticles with L-ascorbic acid (AA) molecules. Oxygen vacancies were tuned in W-VO₂ nanoparticles by thermal annealing temperatures in vacuum, and W-VO_2−x@AA nanoparticles were synthesized by the hydrothermal method. A smart window was formed by dispersing W-VO_2−x@AA core-shell nanoparticles into PVP evenly and spin-coating them on the surface of glass. The visual transmittance of this smart window reaches up to 67%, and the solar modulation reaches up to 12.1%. This enhanced thermo-chromic performance is related to the electron density enhanced by the AA surface molecular coordination effect through W dopant and oxygen vacancies. This work provides a new strategy to enhance the thermo-chromic performance of W-VO₂ and its application in the building energy-saving field. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article