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Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. [ABSTRACT FROM AUTHOR]

Deep learning approaches based on convolutional neural networks (CNNs) have recently achieved success in computer vision, demonstrating significant superiority in the domain of image processing. For hyperspectral image (HSI) classification, convolutional neural networks are an efficient option. Hyperspectral image classification approaches are often based on spectral information. Convolutional neural networks are used for image classification in order to achieve greater performance. The complex computation in convolutional neural networks requires hyper-parameters that attain high accuracy outputs, and this process needs more computational time and effort. Following up on the proposed technique, a bio-inspired metaheuristic strategy based on an enhanced form of elephant herding optimization is proposed in this research paper. It allows one to automatically search for and target the suitable values of convolutional neural network hyper-parameters. To design an automatic system for hyperspectral image classification, the enhanced elephant herding optimization (EEHO) with the AdaBound optimizer is implemented for the tuning and updating of the hyper-parameters of convolutional neural networks (CNN–EEHO–AdaBound). The validation of the convolutional network hyper-parameters should produce a highly accurate response of high-accuracy outputs in order to achieve high-level accuracy in HSI classification, and this process takes a significant amount of processing time. The experiments are carried out on benchmark datasets (Indian Pines and Salinas) for evaluation. The proposed methodology outperforms state-of-the-art methods in a performance comparative analysis, with the findings proving its effectiveness. The results show the improved accuracy of HSI classification by optimising and tuning the hyper-parameters. [ABSTRACT FROM AUTHOR]

The system presented in this paper can change the energy storage landscape by having the advantages of a compressed air storage system and pump storage, as well as minimizing the disadvantages of these two systems. One of the advantages of this system compared to similar systems is the lack of combustion of natural gas. Correspondingly, for construction, it does not require specific specifications for the executive site, and control of the energy and heat of the system (due to the use of water as an operational fluid) is easier than similar systems. In addition, this system is very scalable and can be designed in low capacities to high capacities, energy analysis of this research to identify the basic and effective parameters of the system and determine the limitations and relationships between them. The amount of energy saved in the current research system compared to previous research is significant, and 92% efficiency can be achieved. The energy analysis of this research determined the effect of the parameters on each other and their limitations so that the path of its feasibility design was paved. [ABSTRACT FROM AUTHOR]

Smart agriculture is a concept that refers to a revolution in the agriculture industry that promotes the monitoring of activities necessary to transform agricultural methods to ensure food security in an ever-changing environment. These days, the role of technology is increasing rapidly in every sector. Smart agriculture is one of these sectors, where technology is playing a significant role. The key aim of smart farming is to use the technologies to increase the quality and quantity of agricultural products. IOT and digital image processing are two commonly utilized technologies, which have a wide range of applications in agriculture. IOT is an abbreviation for the Internet of things, i.e., devices to execute different functions. Image processing offers various types of imaging sensors and processing that could lead to numerous kinds of IOT-ready applications. In this work, an integrated application of IOT and digital image processing for weed plant detection is explored using the Weed-ConvNet model to provide a detailed architecture of these technologies in the agriculture domain. Additionally, the regularized Weed-ConvNet is designed for classification with grayscale and color segmented weed images. The accuracy of the Weed-ConvNet model with color segmented weed images is 0.978, which is better than 0.942 of the Weed-ConvNet model with grayscale segmented weed images. [ABSTRACT FROM AUTHOR]

In present day power systems, Power Quality (PQ) issues are causing great concern owing to the increased use of power electronic controlled drives and fluctuating and other non-linear loads. This problem is further aggravated by a steady increase in the integration of renewable energy-based Distribution Generation (DG), employing power electronic converters to distribution systems. Custom power devices with suitable control strategies provide an effective solution to these power quality issues. In this work, a typical three-phase distribution system supplying non-linear load and with DG integration is considered. A shunt connected DSTATCOM at PCC of the system is employed to mitigate power quality concerns. Initially, a parallel-VSI based DSTATCOM configuration, employing individual DC-Link and working basically on the principle of current sharing, has been proposed. The analysis is carried out for variable load conditions for PQ enhancement making use of a more effective control theory viz. Instantaneous Real-Reactive Power (IRP) theory for the generation of suitable switching patterns to the individual VSIs of the parallel DSTATCOM. Further, an improvement over the above configuration viz. combined/common DC-Link-fed parallel DSTATCOM is proposed. This configuration has the advantages of minimized sensing elements, reliable operation and low-cost compensation. A similar analysis is carried out for PQ improvement, making use of the same IRP theory with some modifications (known as MIRP theory). The effectiveness of this configuration is established from the simulation results. In all the above cases, the analyses are carried out using MATLAB/Simulink platform and the simulation results are presented in detail. Thus, the proposed parallel VSIs-based DSTATCOM configurations employing suitable control strategies provide effective solutions for power quality issues under varying load conditions in conventional distribution systems. [ABSTRACT FROM AUTHOR]

Industries have many rotational operations that are used for design, transport, lift, drilling, rolling, robotics, and many other applications. These rotating applications require a proper controller for accurate control of the operation. Separately excited DC motors (SEDCMs) are versatile and have various industrial operations because of their specific speed control characteristics. So, for smooth and accurate operation of an SEDC motor, controllers should be used. PI and PID controllers are used in many cases, but they are ineffective for nonlinear load operation. A fuzzy controller is a heuristic controller and can provide automatic control of the operation. Its operation depends on the selection of the correct membership values. This work proposes a novel particle swarm optimization (PSO) technique that would provide the optimum value of the membership for fuzzy controllers for optimum control of the industrial processes. To obtain SEDC results, MATLAB simulation was performed, and the fuzzy controller with novel PSO was implemented. A fuzzy PSO controller used for motor speed control operation obtains a rise time of 0.00026 s, settling time of 0.000214 s, maximum overshoot of zero, and delay time of 0.016 s, which are the best values when compared to PID and PID-Fuzzy controllers. It is observed that the results obtained from the separately excited DC motor using a fuzzy PSO controller improve the dynamic behavior of the motor that so it smoothly tracks the required speed without any more overshoot or oscillation than the PID controller. Such dynamic, stable operation of the motor makes it perfect for industrial as well as household operations. [ABSTRACT FROM AUTHOR]

Carbon nitride (C2N), a two-dimensional material, is rapidly gaining popularity in the photovoltaic (PV) research community owing to its excellent properties, such as high thermal and chemical stability, non-toxic composition, and low fabrication cost over other thin-film solar cells. This study uses a detailed numerical investigation to explore the influence of C2N-based solar cells with zinc magnesium oxide (Zn1−xMgxO) as a buffer layer. The SCAPS-1D simulator is utilized to examine the performance of four Mg-doped buffer layers (x = 0.0625, 0.125, 0.1875, and 0.25) coupled with the C2N-based absorber layer. The influence of the absorber and buffer layers' band alignment, quantum efficiency, thickness, doping density, defect density, and operating temperature are analyzed to improve the cell performance. Based on the simulations, increasing the buffer layer Mg concentration above x = 0.1875 reduces the device performance. Furthermore, it is found that increasing the absorber layer thickness is desirable for good device efficiency, whereas a doping density above 1015 cm−3 can degrade the cell performance. After optimization of the buffer layer thickness and doping density at 40 nm and 1018 cm−3, the cell displayed its maximum performance. Among the four structures, C2N/Zn0.8125Mg0.1875O demonstrated the highest PCE of 19.01% with a significant improvement in open circuit voltage (Voc), short circuit density (Jsc), and fill factor (FF). The recorded results are in good agreement with the standard theoretical studies. [ABSTRACT FROM AUTHOR]

The usage of plastic materials in our daily life is increasing day by day. These plastic materials are somehow beneficial for us, but the disposal of waste plastic materials has become a serious problem. The use of plastic not only enhances road construction but also helps extend the life of roads and improves the environment. Waste plastics use in roads increases durability and also reduces water retention. This research reviews the use of waste plastics in asphalt pavement. In this study, the properties such as Marshall stability, flow, resilient modulus, fatigue, etc., are studied to boost the usage of waste plastic in asphalt pavements. It is concluded that with the use of waste plastic in asphalt pavement, the quality of roads will be enhanced, and it will also be very beneficial for our environment. The other major advantage is that it will be very cost-effective for underdeveloped countries. [ABSTRACT FROM AUTHOR]

Due to the prospect of climate change and the challenges posed by the European Union to the modern power grid, a decentralized system based on distributed energy sources is being created from a centralized system based on utility power. It also involves new ideas on the operation and management of power grids, especially at the level of low-voltage distribution networks, where prosumers play a special role. In addition to the transformation of sources to renewables, the aim is to increase the flexibility of power grids by exploiting the regulatory potential of flexible grid components. The issue of grid flexibility assumes particular importance in the case of microgrids and local grids covered by the energy communities. Many posts describe the realization of the task of flexibility through energy storage, e.g., storing storage resources in electric vehicles or the use of energy transformation through conversion to heat, air compression air, or process cooling. However, there seems to be a lack of exploration of the topic, where the photovoltaic inverter could provide a flexible energy source while maintaining the rigor of power quality. This article presents current developments in low-voltage grids and the prospect of using prosumer installations to provide grid flexibility and stability. [ABSTRACT FROM AUTHOR]

To lessen the amount of energy lost during transmission, electricity is increasingly being sent using high-voltage lines. Transmission loss in a DC system is lower than in an AC system over long distances. The DC system can improve the efficiency of long-distance transmission by connecting power grids with different requirements. The DC method is becoming popular since it helps to keep the grid stable. Managing and blocking DC flow is crucial to system functionality. In this study, we explore the operation of a flux-coupled type superconducting fault current limiter (SFCL) in a DC system, where the two windings are connected in parallel to limit the fault current flow. A flux-coupled type SFCL is built by connecting two coils in parallel and a superconducting element (SE) in series with the secondary coil. The functions of an SFCL of the flux-coupled kind are equivalent in both direct and alternating current systems. Because of the opposing magnetic fluxes produced by the two coils, the voltage generated by the parallel connection of the coils is always zero. Inadequate SE leads to an increase in resistance, inhibiting the cancellation of opposing magnetic fluxes and hence a loss in power. Connecting the two coils in series allows voltage to be generated while the fault current is limited. To further validate the performance of SFCL with varying resistance and inductance, the system is additionally tested on the IEEE 39 bus system. The MATLAB/SIMULINK software suite is used to run the test system. [ABSTRACT FROM AUTHOR]

This article presents an idea of the implementation of different real load profiles for energy storage system (ESS) operation. The considered approaches are based on real long-term measurements using energy meters, the adaptation of the standard profiles defined by the distribution system operator (DSO), as well as a mix of the level of contracted power and short-term measurements. All combinations are used as electricity demand to formulate an ESS operation plan that cooperates with the PV system and the electricity market. The GAMS solver is applied to obtain optimal operation tasks of the ESS to cover different real and pseudo-real load profiles of an industrial company. Obtained results are presented using a real case study of a metallurgy company with a 317 kWp photovoltaic installation and a 200 kW ESS. [ABSTRACT FROM AUTHOR]

Power quality (PQ) becomes a more and more pressing issue for the operation stability of power systems with renewable energy sources. An important aspect of PQ monitoring of distribution networks is to compare the PQ indicators in different operating conditions. This paper evaluates the impact of a microgrid implementation in a real distribution network on power quality indicators at the point of common coupling in an LV network. The study includes a classical assessment of the long-term PQ parameters according to the EN 50160 standard, such as nominal frequency deviations, voltage RMS variations, voltage fluctuations (represented by long-term flicker severity), voltage unbalance and total harmonic distortion. The PQ evaluation is extended in statistical assessment based on cluster analysis. The case study contains 5 weeks of power quality observation results obtained at the assessment point in two different working conditions of the distribution system: before and after implementing the microgrid. The study allows establishing general conclusions regarding a microgrid interconnection in order not to exceed power quality limits and considering the influence of photovoltaic generation on power quality parameters. [ABSTRACT FROM AUTHOR]

The journal Energies has retracted an article titled "Investigation and Evaluation of the Hybrid System of Energy Storage for Renewable Energies" due to concerns of overlap with a previously published manuscript in another language. An investigation conducted by the Editorial Office, members of the Editorial Board, and the Editor-in-Chief confirmed significant overlap, leading to the retraction of the article. Some authors agree with the retraction, while others disagree, and the remaining authors did not provide a comment on the decision. [Extracted from the article]

This study used panel simultaneous equations models with a generalized method of moments (GMM) estimator to examine the three-way linkages between ecological footprint (EFP), renewable energy consumption (REC), and income in the Group of Seven (G7) countries over the period 1990–2018. The outcomes of this study demonstrate a two-way association between gross domestic product (GDP) and renewable energy. The findings confirm the presence of a bidirectional link between outcome and ecological footprint, as well as between EFP and renewable energy. The results of this study demonstrate that improving human capital positively and significantly effects income, environmental quality, and REC. Ecological footprint is not significantly impacted by economic and social globalization, whereas the impact of financial globalization is negative and significant. Trade openness is positively and significantly connected with REC and income, which could contribute to reducing environmental deterioration. In conclusion, we make policy recommendations that are based on the findings of the study. [ABSTRACT FROM AUTHOR]

Energy is essential to achieving economic growth, yet the production of energy results in the emission of carbon dioxide, the primary factor in the deterioration of the environment and the acceleration of climate change. In this sense, the diversity of energy sources can contribute to achieving both environmentally sustainable development. This study investigates the relationship between energy diversification and economic growth in Nordic nations by employing a unique measure of energy diversity. The Nonlinear Panel Autoregressive Distributed Lag (NPARDL) approach is utilized in the research, and it looks at data from 1998 through 2018. According to our results, these nations experience favorable economic growth when there is an increase in the long-term diversity of their energy sources. However, in the near term, they have seen negative economic development due to the diversification of their energy sources. According to these findings, energy diversification benefits Nordic economic growth; however, further research is required for developing economies. As a result, further preventative actions must be implemented while simultaneously diversifying energy sources. [ABSTRACT FROM AUTHOR]

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented. [ABSTRACT FROM AUTHOR]

The impact of mining industries' operations on different areas is easy to be noticed. Thus, nowadays different research considering ecology, economy, society, or engineering is performed in light of mining industries. However, the impact on education development is not the first choice. Thus, the aim of this paper is the verification of possibility to indicate the main key factors (activities) in the mining industry, that are in accordance with the CSR doctrine and are capable of ensuring education development. To reach this purpose, KGHM Polska Miedź SA was selected for the research, as an example of a mining company operating on a global scale. Then, the appropriate CSR standard "Principles for Global Corporate Responsibility, Bench Marks for Measuring Business Performance" was a basis for the operationalization process to assure the research tool, that defines the list of the activities in accordance with CSR doctrine. The obtained research tool was used to perform the survey, which indicated the key factors that assure education development. As a result, over 65 key factors were indicated. Generally, the factors considered, e.g., free access to education for workers, suppliers, and vendors, do not affect the education of children and young workers, as does preparing company documents in language adequate to the operating area. [ABSTRACT FROM AUTHOR]

An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%. [ABSTRACT FROM AUTHOR]

In this research, we proposed a Deep Convolutional Neural Network (DCNN) model for image-based plant leaf disease identification using data augmentation and hyperparameter optimization techniques. The DCNN model was trained on an augmented dataset of over 240,000 images of different healthy and diseased plant leaves and backgrounds. Five image augmentation techniques were used: Generative Adversarial Network, Neural Style Transfer, Principal Component Analysis, Color Augmentation, and Position Augmentation. The random search technique was used to optimize the hyperparameters of the proposed DCNN model. This research shows the significance of choosing a suitable number of layers and filters in DCNN development. Moreover, the experimental outcomes illustrate the importance of data augmentation techniques and hyperparameter optimization techniques. The performance of the proposed DCNN was calculated using different performance metrics such as classification accuracy, precision, recall, and F1-Score. The experimental results show that the proposed DCNN model achieves an average classification accuracy of 98.41% on the test dataset. Moreover, the overall performance of the proposed DCNN model was better than that of advanced transfer learning and machine learning techniques. The proposed DCNN model is useful in the identification of plant leaf diseases. [ABSTRACT FROM AUTHOR]

Steady increase in electricity consumption, fossil fuel depletion, higher erection times of conventional plants, etc., are encouraging the use of more and more onsite renewable energy. However, due to the dynamic changes in environmental factors as well as the customer load, renewable energy generation is facing issues with reliability and quality of the supply. As a solution to all these factors, renewable energy integrated cluster microgrids are being formed globally in urban communities. However, their effectiveness in generating quality power depends on the power electronic converters that are used as an integral part of the microgrids. Thus, this paper proposes the "Fuzzy Hysteresis Current Controller (FHCC)-based Inverter" for improving the power quality in renewable energy integrated cluster microgrids that are operated either in grid-connected or autonomous mode. Here, the inverter is controlled through a fuzzy logic-based hysteresis current control loop, thereby achieving superior performance. System modelling and simulations are done using MATLAB/Simulink®. The performance analysis of the proposed and conventional inverter configurations is done by computing various power quality indices, namely voltage characteristics (swell, sag, and imbalance), frequency characteristics (deviations), and total harmonic distortion. The results reveal that the proposed FHCC-based inverter achieves a better quality of power than the traditional ST-PWM-based multilevel inverter in terms of IEEE/IEC/EN global standards for renewable energy integrated cluster microgrids application. [ABSTRACT FROM AUTHOR]

This article is devoted to the relevant problem of increasing the efficiency of PV systems. The presented analysis discusses the available methods for improving the power generation of PV modules under partial shading. Mathematical models for power loss calculation were compiled based on the results of this analysis. The proposed approach minimizes the negative impact of partial shading on the energy production of PV modules. It is based on the equalization of voltages of parallel-connected arrays of modules by installing additional power elements in them. The proposed solution is promising for various areas; it allows for the minimization of the unfavorable influence of existing urban objects (buildings, trees, communications, etc.) on the energy efficiency of PV modules. The obtained results are useful for the sustainable development of the urban environment in the context of digital transformation. They are the basis for the promising methodology of the parametric optimization of power plants using renewable energy sources. [ABSTRACT FROM AUTHOR]

An isolated micro-grid has different requirements from the traditional power grids. Several energy sources may be linked for the purpose of sharing load demand without being linked to the grid. The isolated micro-grid is made up of at least one energy generator, an energy storage system, and a load portion. Because there are several energy sources and a range of models, the power flow must be managed to ensure the safety of all hardware. Monitoring the flow of power from multiple energy sources necessitates adherence to many parameters and other requirements. As a result, the goal of this work is to identify a worthwhile solution for providing the appropriate portions with the necessary power while also obtaining the necessary energy from other sources. The approach is based on the fuzzy logic controller, which is an intelligent technology. This regulator is used in an efficient process that tries to control all of the equipment in the isolated micro-grid under investigation. The MATLAB/Simulink platform is employed for simulating this proposed system, and then, the depicted results were discussed and compared. Showing the traditional relay control, the standard PI regulator, and a neural control combination process, the achieved results prove that it is possible to reduce the battery recharge time to half; if the proposed fuzzy controller is used. Then, the established controller specifications have been used for evaluating the energy performances of the hybrid energy system under a real case situation in a specific location in the world. Consequently, the obtained results prove that this proposal power management system will be largely beneficial for such energy storage applications and an energy yield can be assured during all climatic conditions and specifications. [ABSTRACT FROM AUTHOR]

The idea of the call for the Special Issue "Signal Analysis in Power Systems" came from scholarly discussions about ever increasing complexity of the management and operation of today's power system. Paper [[4]] presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multi-parameter analysis of power quality data addressed to particular measurement points. [Extracted from the article]

In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions. [ABSTRACT FROM AUTHOR]

The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents' pollution. Usually, to forecast the modern electrical and electronic devices' contribution to increasing the current total harmonic distortion factor (T H D I) and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities' nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor (T H D I) . The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear loads should be a key factor in harmonic currents' pollution study. Proper forecasting of the level of harmonic currents injected into the utility grid helps to maintain the quality of electricity at an appropriate level and reduce active power losses, which have a direct impact on the price of electricity generation. [ABSTRACT FROM AUTHOR]

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems. [ABSTRACT FROM AUTHOR]

A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers' premises. The contribution of these female home-based workers (FHBWs) is significant to the country's economic growth. FHBWs are often confronted with numerous occupational diseases due to a lack of awareness of occupational safety and health measures, and unhealthy living and working conditions. The informality of FHBWs prevents them from getting proper healthcare, safety, and other dispensations enjoyed by formal employees. Despite their undeniable importance, health issues of FHBWs are still overlooked. This study is an attempt to discover the frequent co-occurring occupational diseases encountered by FHBWs in Punjab, a province of Pakistan. Frequent itemset mining (FIM) or co-occurrence grouping is a technique of data science that identifies the associations among different entities in the data. Based on FIM, the D-GENE algorithm is applied in this study to efficiently discover frequent co-occurring diseases in the data obtained from the Punjab Home-based Workers Survey (2016). The far-reaching goal of the study is to bring awareness of the occupational health issues and safety risks to the health authorities as well as to the FHBWs. [ABSTRACT FROM AUTHOR]

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units' relation in point of PQ. Under the investigation, both Pearson's and Spearman's rank correlation coefficients were considered. [ABSTRACT FROM AUTHOR]

One of the recent trends that concern renewable energy sources and energy storage systems is the concept of virtual power plants (VPP). The majority of research now focuses on analyzing case studies of VPP in different issues. This article presents the investigation that is based on a real VPP. That VPP operates in Poland and consists of hydropower plants (HPP), as well as energy storage systems (ESS). For specific analysis, cluster analysis, as a representative technique of data mining, was selected for power quality (PQ) issues. The used data represents 26 weeks of PQ multipoint synchronic measurements for 5 related to VPP points. The investigation discusses different input databases for cluster analysis. Moreover, as an extension to using classical PQ parameters as an input, the application of the global index was proposed. This enables the reduction of the size of the input database with maintaining the data features for cluster analysis. Moreover, the problem of the optimal number of cluster selection is discussed. Finally, the assessment of clustering results was performed to assess the VPP impact on PQ level. [ABSTRACT FROM AUTHOR]

The integration of virtual power plants (VPP) has become more popular. Thus, research on VPP for different issues is highly desirable. This article addresses power quality issues. The presented investigation is based on multipoint, synchronic measurements obtained from five points that are related to the VPP. This article provides a proposition and discussion of using one global index in place of the classical power quality (PQ) parameters. Furthermore, in the article, one new global power quality index was proposed. Then the PQ measurements, as well as global indexes, were used to prepare input databases for cluster analysis. The mentioned cluster analysis aimed to detect the short-term working conditions of VPP that were specific from the point of view of power quality. To realize this the hierarchical clustering using the Ward algorithm was realized. The article also presents the application of the cubic clustering criterion to support cluster analysis. Then the assessment of the obtained condition was realized using the global index to assure the general information of the cause of its occurrence. Furthermore, the article noticed that the application of the global index, assured reduction of database size to around 74%, without losing the features of the data. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the time to repair failures, and the time for connection harmonization. All these constituents have been considered more specifically. The main constituents' results values of the power supply restoration time were analyzed for the electrical networks of regional power supply company "Oreolenergo", a branch of Interregional Distribution Grid Company (IDGC) of Center. The Delphi method was used for determining the time for obtaining information on failures as well as the time for information recognition. The method of mathematical statistics was used to determine the repair time. The determined power supply restoration time (5.28 h) is similar to statistical values of the examined power supply company (the deviation was equal to 9.9%). The technical means of electrical network automation capable of the reduction of the power supply restoration time have also been found. These means were classified according to the time intervals they shorten. [ABSTRACT FROM AUTHOR]

This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification. [ABSTRACT FROM AUTHOR]

This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points. [ABSTRACT FROM AUTHOR]

This paper analyzes the technical and economic possibilities of integrating distributed energy resources (DERs) and energy-storage systems (ESSs) into a virtual power plant (VPP) and operating them as a single power plant. The purpose of the study is to assess the economic efficiency of the VPP model, which is influenced by several factors such as energy price and energy production. Ten scenarios for the VPP were prepared on the basis of the installed capacities of a hydropower plant (HPP), rooftop solar photovoltaic (PV), and energy-storage system (ESS), as well as weather conditions, in Poland. On the basis of technical conditions, it was assumed that the maximum power capacity of the ESS equaled 1.5 MW. The economic efficiency analysis presented in this paper demonstrated that, in seven years, the VPP will achieve a positive value of the net present value (NPV) for a scenario with 0.5 MW battery storage and rainy summers. Furthermore, sensitivity analysis was conducted on price factors and DER production volume. The price variable had a major impact on the NPV value for all scenarios. The scenario with a 0.5 MW battery and typical summers was highly sensitive to all factors, and its sensitivity decreased as the ESS capacity grew from 0.5 to 1.5 MW. [ABSTRACT FROM AUTHOR]

Recently a number of changes were introduced in amendment to standard EN 50160 related to power quality (PQ) including 1 min aggregation intervals and the obligation to consider 100% of measured data taken for the assessment of voltage variation in a low voltage (LV) supply terminal. Classical power quality assessment can be extended using a correlation analysis so that relations between power quality parameters and external indices such as weather conditions or power demand can be revealed. This paper presents the results of a comparative investigation of the application of 1 and 10 min aggregation times in power quality assessment as well as in the correlation analysis of power quality parameters and weather conditions and the energy production of a 100 kW photovoltaic (PV) power plant connected to a LV network. The influence of the 1 min aggregation time on the result of the PQ assessment as well as the correlation matrix in comparison with the 10 min aggregation algorithm is presented and discussed. [ABSTRACT FROM AUTHOR]