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With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

The development of wind energy has been observed for many years. Both construction firms and the scientific world are analyzing new design solutions, atmospheric conditions and the technical performance achieved. The main goal of this research is to evaluate the requirements that have to be met to design wind power stations that would be an optimal fit for the climatic conditions in Poland. This study combines the results of empirical studies on wind velocity distributions with the physical fundamentals of wind power station design. This paper presents modelling of the relationships between wind velocity distributions observed in Poland and technical requirements for wind power stations design. The wind velocities distributions for various locations in Poland are determined and expressed in Weibull distribution parameters. Theoretical computations concerning the dependence of wind power stations as function of wind speed and air's physical properties are presented. Conclusions important for the design of power stations fitted to the atmospheric conditions in Poland are given. LabVIEW 2021 was used for computer modeling. [ABSTRACT FROM AUTHOR]

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs. [ABSTRACT FROM AUTHOR]

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field. [ABSTRACT FROM AUTHOR]

This paper presents a methodology developed to perform the processing, analysis, and characterization of AMI measurement data from the substations of three network operators of the Colombian electrical grid. This methodology includes the analysis of the data, which presents the sources of information used by the model, along with the preprocessing and exploratory analysis of the substations data. It also includes the formulation of the data reconstruction method, which uses a constrained optimization model to characterize the substations, based on the different behaviors of the end users of the Colombian electrical grid. In addition to the proposed methodology, the results of its application to the data provided by the operators are provided. These results show the capacity of the proposed methodology to adequately identify the most common behaviors of the users in a given area and characterize most of the energy demand profiles of each substation. [ABSTRACT FROM AUTHOR]

Energy is consumed in buildings through the use of various types of energy systems, which are controlled by the occupants via provided interfaces. The quality of this control should be verified to improve the efficiency of the systems and for the comfort of the occupants. In the case of residential buildings, due to privacy reasons, it is problematic to directly monitor human–building interactions using sensors installed in dwellings. However, data from increasingly common smart meters are easily available. In this paper, the potential use of data from heat meters is explored for the analysis of occupant interactions with space-heating (SH) systems. A pilot study is conducted based on a one-year set of daily data from 101 dwellings. First, the identification of an indoor temperature and a strategy for thermostatic radiator valve (TRV) adjustments for all the investigated dwellings is presented. Second, the performed analysis suggests that 96% of the households did not use the automatic adjustment function of the TRVs since adjustments using the on–off mode were the most common, which could be empirical evidence for Kempton's theory on mental models of home heating controls. The reasons for this could be the weakness of the TRV as an SH interface and the technical specificity of the analyzed SH (its supply temperature). The preliminary investigation confirms the potential of the proposed methodology, but further research is needed. [ABSTRACT FROM AUTHOR]

Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data. [ABSTRACT FROM AUTHOR]

This paper aims to investigate and compare the fuel consumption data obtained from on-board fuel consumption meters (OBFCMs) from approximately 1000 vehicles through field testing. Furthermore, this research aims not only to compare the OBFCM readings but also to juxtapose them against the fuel consumption specifications provided by the respective vehicle manufacturers. To collect data, a cost-effective on-board diagnostics (OBD) reader and a user-friendly mobile app were employed, providing an accessible and efficient method for fuel consumption analysis. Field testing involved a diverse range of vehicles, covering various makes, models, and years of production. The OBCFM readings were recorded over a 9-month period, probably capturing a wide range of driving conditions and patterns. In order to ensure the reliability of the OBCFM readings, the fuel consumption measurements obtained from the manufacturers specifications were utilized as a reference benchmark. Preliminary data analysis indicates that there are noticeable variations in the fuel consumption data obtained from the OBCFM and the manufacturer specifications. These differences can be attributed to various factors. The novelty of the presented data lies in using a new feature implemented in EU cars since 2019. The study capitalizes on this feature, allowing for the collection of data from a broad spectrum of vehicles throughout the country under genuine driving conditions. [ABSTRACT FROM AUTHOR]

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations. [ABSTRACT FROM AUTHOR]

The main purpose of the paper was to identify the most frequently discussed directions of research on green transformation. In the article, both the significant similarities in the existing studies in this field, as well as the newly emerging topics of research, are presented. For this purpose, the authors used a systematic literature review with elements of statistical analyses. This kind of approach is not popularly used in literature review papers, as it differs from the research practices employed previously, which mostly concentrated on applying qualitative methods, alternatively supported by the analysis of the co-occurrence of keywords. In this paper, the authors decided to include selected methods of dimensional analysis in the systematic literature review, namely the log-linear and correspondence analyses. The main results of the presented analyses are a more detailed division of studies related to green transformations into groups focused on the areas more difficult to distinguish in terms of the traditionally conducted literature review. [ABSTRACT FROM AUTHOR]

Generally, PV (photovoltaic) modules are known as devices which are used semi-permanently for more than 20 years, but the electrical performance and lifespan of PV modules can be significantly degraded due to various environmental factors. Thus, a proper evaluation method for aging phenomenon of PV modules is required. Although there already are methods which compare adjusted PV output power based on STC (standard test condition) with initial PV module specification, or perform direct comparison by conducting the test under STC, there are issues with objectivity or efficiency in the existing evaluation method of aging phenomenon due to the data distortion while adjusting measured data or difficulties in implementation. Therefore, in order to overcome the above-mentioned disadvantage of the existing evaluation method for deterioration in PV modules and evaluate the aging characteristics of PV modules based on on-site measurement data in an accurate and efficient manner, this paper implements a test device for aging diagnosis to measure and collect actual data from a PV module section, and presents a modeling of data analysis for aging phenomenon with MATLAB S/W in order to minimize the variability of PV output, communication error, delay, etc. Furthermore, this paper confirms the usefulness of the presented test device for aging diagnosis of the PV modules which is accurately evaluated by considering on-site measurement of PV output power by season. [ABSTRACT FROM AUTHOR]

Wind turbines are expected to provide on the order of 50% of the electricity worldwide in the near future, and it is therefore fundamental to reduce the costs associated with this form of energy conversion, which regard maintenance as the first item of expenditure. SCADA-based condition monitoring for anomaly detection is commonly presented as a convenient solution for fault diagnosis on turbine components. However, its suitability is generally proven by empirical analyses which are limited in time and based on a circumscribed number of turbines. To cope with this lack of validation, this paper performs a controlled experiment to evaluate the suitability of SCADA-based condition monitoring for fault diagnosis in a fleet of eight turbines monitored for over 11 years. For the controlled experiment, a weakly supervised method was used to model the normal behavior of the turbine component. Such a model is instantiated as a convolutional neural network. The method, instantiated as a threshold-based method, proved to be suitable for diagnosis, i.e. the identification of all drivetrain failures with a considerable advance time. On the other hand, the wide variability between the time the alarm is raised and the fault is observed suggests its limited suitability for prognosis. [ABSTRACT FROM AUTHOR]

In this paper, the basic structure of an ICT platform of energy indicators, Openergy, is analytically presented, leveraging energy open data to help address the energy crisis more democratically. More specifically, its applicability as a dynamic tool for the management of climate, environmental, and socioeconomic information is described, and its efficiency in helping uncover insights for optimal data-driven decisions is depicted. Openergy uses data from the official portal for European data and the Eurostat site. Its database consists of data related to six energy categories, EU 2020 energy targets, energy balance, electricity production, transport fuels, heat production, and gas emissions, and each one includes its own indicators for EU countries. The platform includes visualizations of these data as well as time series modeling and forecasting, and the results are depicted at Openergy platform. The time series modeling provides forecasts with confidence intervals of each indicator until 2020 in each energy category. Empirical validation, RMSE, and MAE values showed that in almost all cases and estimations, the predicted values are in very good agreement with the observed values. [ABSTRACT FROM AUTHOR]

Energy determines the social, economic, and environmental aspects that enable the advancement of communities. For this reason, this paper aims to analyze the quality of the energy service in the Non-Interconnected Zones (NIZ) of Colombia. For this purpose, clustering techniques (K-means, K-medoids, divisive analysis clustering, and heatmaps) are applied for data analysis in the context of the NIZ to identify patterns or hidden information in the Colombian government data related to the state of the electricity service in these localities during the years 2019–2020. A descriptive statistical analysis and validation of the results of the clustering techniques is also carried out using R software. Through the implementation of clustering algorithms such as K-means, K-medoids, and divisive analysis clustering, potential areas for the development of renewable and alternative energy projects are identified, considering places with deficiencies in their current electricity service, higher consumption, or places with very low daily hours of electricity service. Additionally, relationships were identified in the dataset that can be considered as tools that would support decision-making for academia and industry, as well as the definition of guidelines or strategies from the government to improve energy efficiency and quality for these places, and consequently, the living conditions of the residents of Colombia's NIZs. [ABSTRACT FROM AUTHOR]

It is a truism that whole energy system models underpin the development of policies for energy system decarbonisation. However, recent reviews have thrown doubt on the appropriateness of such models for addressing the multiple goals for future energy systems, in the face of emergent real-world complexity and the evolution of stakeholder's priorities. Without an understanding of the changing priorities of policy makers and expectations of stakeholders for future systems, system objectives and constraints are likely to be ill-defined, and there is a risk that models may be inadvertently instrumentalised. Adopting a system architecture perspective, the authors have undertaken a three-year programme of research to explore strategies for decarbonising heat in the UK, with interaction with and elicitation of needs from stakeholders at its heart. This paper presents the procedure, methods, and results of an exercise in which experts from stakeholder organisations across the energy system were interviewed. Analysis of interview data reveals two broad approaches to heat decarbonisation which can be defined as either adaptive or transformative. Specific insights gained from these interviews enabled our modelling teams to refocus their work for exploration with a wider circle of stakeholders. Results suggests that this iterative approach to formalising model-policy interaction could improve the transparency and legitimacy of modelling and enhance its impact on policy making. [ABSTRACT FROM AUTHOR]

The use of electric vehicles and their adoption have become imperative due to the policies and goals to reduce harmful emissions emitted by fossil fuel vehicles. The adoption and acceptance of electric vehicles by the public may be determined by several factors, such as the price of the vehicles, the environmental and political costs of vehicle production, infrastructure availability, etc. However, given the diverse context of each country, different factors may have a different impact on customer attitudes. According to that fact, the purpose of this research is to identify which factors influence attitudes towards the purchase of electric vehicles in the Republic of Croatia. In order to identify those factors, this paper is based on a survey in which 578 randomly selected respondents took part. The research was conducted between 1 October 2021 and 1 May 2022. The obtained research results were analyzed using linear regression, i.e., moderation analysis. The results indicated that the most significant factors influencing attitudes towards the purchase of electric cars are difficulties in servicing, the cost of battery replacement, lack of charging infrastructure, and the reduced impact on environmental pollution. The basic conclusion of this research is that it is necessary to invest more in the development of infrastructure that supports the use of electric vehicles and in the development of electric vehicles themselves and their characteristics. [ABSTRACT FROM AUTHOR]

Motor diagnostics is an important subject for consideration. Electric motors of different types are present in a multitude of object, from consumer goods through everyday use devices to specialized equipment. Diagnostic assessment of motors using acoustic signals is an interesting field, as microphones are present everywhere and are relatively easy sensors to process. In this paper, we analyze acoustic signals for the purpose of motor diagnostics using functional data analysis. We represent the spectrum (FFT) of the acoustic signals on a B-Spline basis and construct a classifier based on that representation. The results are promising, especially for binary classifiers, while multiclass (softmax regression) shows more sensitivity to dataset size. In particular, we show that while we are able to obtain almost perfect classification for binary cases, multiclass classifiers can struggle depending on the training/testing split. This is especially visible for determining the number of broken teeth, which is a non-issue for binary classifiers. [ABSTRACT FROM AUTHOR]

Energy management and monitoring systems are significant difficulties in applying microgrids to smart homes. Thus, further research is required to address the modeling and operational parts of the system's future results for various applications. This paper proposes a new technique for energy management in a microgrid using a robust control approach and the development of a platform for real-time monitoring. The developed controller is based on a fuzzy logic method used in the energy Internet paradigm with connected distributed generators (DGs) in the microgrid. The developed method regulates the power flow of the microgrid, and frequency/voltage regulation improved the load-management performance and monitoring system using the ThingSpeak platform for real-time data analysis. The MATLAB. simulation results show the feasibility and effectiveness of the proposed strategy and the introduced approach in microgrid control under various operating conditions. Additionally, the results show that the proposed monitoring platform facilitates real-time data analysis. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively. [ABSTRACT FROM AUTHOR]

The research reported in this paper involves the development and refinement of methods applicable to the measurement and analysis of infrasound signals generated by the operation of wind turbines. In particular, the presentation focuses on the use of a new system that is applied for simultaneous recording of acoustic signals in the low-frequency range emitted by wind farms in three independent and identical measurement setups. A comparative analysis of the proposed new system was made with the Brüel & Kjaer measurement, a commonly used methodology, which meets the requirements of the IEC 61400-11 standard. The paper focuses on the results of frequency and time-frequency analysis of infrasound signals recorded throughout the operation of a wind turbine with a rated capacity of 2 MW. The use of a correlated system with three simultaneous measurement systems can be a new and alternative measurement method that will eliminate the drawbacks of previous approaches. [ABSTRACT FROM AUTHOR]

This paper presents a comparison study between the measured solar radiations on site and the calculated solar radiation based on the most theoretical models presented in the literature up to date. Indeed, for such purposes, this paper focusses on the analysis of the data of the measured solar radiation collected on two sites in Algeria such as Tlemcen (34°52′58″ N 01°19′00″ W, elevation 842 m) and Senia (35°39′ N 0°38′ W, elevation: 77 m). In order to check the accuracy of the proposed model, the experimental collected data of the solar radiation obtained from the existing radiometric stations installed at the two locations under investigation, are compared with the estimated or predicted solar radiations obtained from the Capderou and R.Sun models, where four days under clear skies are selected from different seasons to achieve this comparison. Second, the daily averages of the experimental global solar irradiation are compared to those predicted by Mefti model for both the sites. Finally, a validation is carried out based on the obtained experimental monthly global irradiations and with those estimated by Coppolino and Sivkov models. A relative difference is used in this case to judge the reliability and the accuracy of each model for both sites. [ABSTRACT FROM AUTHOR]

The aim of the study is to explore the intellectual structure of the field and fronts in research on energy efficiency in the context of cloud computing and thus to contribute to science mapping of the research field. The research process was driven by the following study questions: (1) what are the most influential publications in the research field? and (2) what are the research fronts in the research field? The method of direct citation analysis was employed in the research process. Data for analysis were obtained from the Scopus database and analyzed with the use of VOSviewer science mapping software. In response to the first question, we identified the most influential publications in the research field and analyzed their types (i.e., whether they are original research papers or rather the "context" papers e.g., survey or review papers, framework papers, challenges papers, and study papers). Moreover, a comparison analysis between the types of papers among the most cited "classical" publications and "emerging stars" was conducted. In response to the second research question, we identified five research fronts concentrated around the issues of: virtual machine management ("VM"); task-focus, concerning data replication, task consolidation, and task scheduling ("task"); energy efficiency ("energy"); modelling and optimization ("model"); and energy efficiency in the networking context ("network"). [ABSTRACT FROM AUTHOR]

In the quest to achieve sustainable pipeline operations and improve pipeline safety, effective corrosion control and improved maintenance paradigms are required. For underground pipelines, external corrosion prevention mechanisms include either a pipeline coating or impressed current cathodic protection (ICCP). For extensive pipeline networks, time-based preventative maintenance of ICCP units can degrade the CP system's integrity between maintenance intervals since it can result in an undetected loss of CP (forced corrosion) or excessive supply of CP (pipeline wrapping disbondment). A conformance evaluation determines the CP system effectiveness to the CP pipe potentials criteria in the NACE SP0169-2013 CP standard for steel pipelines (as per intervals specified in the 49 CFR Part 192 statute). This paper presents a predictive maintenance framework based on the core function of the ICCP system (i.e., regulating the CP pipe potential according to the NACE SP0169-2013 operating window). The framework includes modeling and predicting the ICCP unit and the downstream test post (TP) state using historical CP data and machine learning techniques (regression and classification). The results are discussed for ICCP units operating either at steady state or with stray currents. This paper also presents a method to estimate the downstream TP's CP pipe potential based on the multiple linear regression coefficients for the supplying ICCP unit. A maintenance matrix is presented to remedy the defined ICCP unit states, and the maintenance time suggestion is evaluated using survival analysis, cycle times, and time-series trend analysis. [ABSTRACT FROM AUTHOR]

In a modern smart energy system, the amount of available data from various sources is growing significantly. Other sectors such as medical or social sectors exhibit the same phenomenon. Due to the amount, complexity and heterogeneity of data, a complex algorithm is required for the integration and analysis of heterogeneous data sources. The Web of Things and semantic-based approaches address the fragmentation of standards, platforms, services and technologies in smart energy and non-energy sectors, and enable heterogeneous data integration and interoperability. This paper presents SiSEG, a semantic annotation service that is developed to automate the process of annotating data and address the problem of heterogeneous data integration in a reusable and extensible way by using the fuzzy method. Moreover, the accuracy of SiGEG has been evaluated. [ABSTRACT FROM AUTHOR]

The rapid increase of electric vehicles (EVs) would lead to a rise in load demand on power grids but create different potential benefits as well. Those benefits comprise EVs serving as a mobile energy storage system to participate in adjusting the load on the power grids and helping manage renewable energy resources. This paper evaluates the effect of dynamic electricity prices and home photovoltaic (PV) system incentives on users' EVs charging behavior and potential impacts on grid load and household economy. This has been done by establishing and assessing three different optimized charging configurations and comparing them to an uncontrolled charging strategy. In this study, the charging incentives are applied to a representative sample of 100 households with EVs and PV systems in a metropolitan area. The results show that an optimized charging strategy based on the dynamic electricity tariff can reduce charging costs by 18.5%, while a PV-based optimized strategy can reduce the costs by 33.7%. Moreover, the PV-integrated optimization strategies significantly increase the utilization of PV energy by almost 46% on average, compared to uncontrolled charging. In addition, the simulations of this research have depicted the capability of using home PV systems' incentives to smoothen the charging profiles and hence significantly reduce the maximum grid load. However, the electricity price optimization strategy increases the aggregated charging peaks, which can only be slightly reduced by peak shaving. Therefore, an identical price signal for all households might be critical. Further analyses have shown that direct charging occurs simultaneously with household electricity assigned to a specific low-voltage grid while PV and price incentive charging configurations shift the charging peaks away from household load peaks. [ABSTRACT FROM AUTHOR]

The superheater and re-heater piping components in supercritical thermal power units are prone to creep and fatigue failure fracture after extensive use due to the high pressure and temperature environment. Therefore, safety assessment for superheaters and re-heaters in such an environment is critical. However, the actual service operation data is frequently insufficient, resulting in low accuracy of the safety assessment. Based on such problems, in order to address the issues of susceptibility of superheater and re-heater piping components to creep, inaccurate fatigue failure fracture, and creep–fatigue coupling rupture in a safety assessment, their remaining life prediction and reliability, as well as the lack of actual service operation data, multisource heterogeneous data generated from actual service of power plants combined with deep learning technology was used in this paper. As such, three real-time operating conditions' data (temperature, pressure, and stress amplitude) during equipment operation are predicted by training a deep learning architecture long short-term memory (LSTM) neural network suitable for processing time-series data and a backpropagation through time (BPTT) algorithm is used to optimize the model and compared with the actual physical model. Damage assessment and life prediction of final superheater tubes of power station boilers are carried out. The Weibull distribution model is used to obtain the trend of cumulative failure risk change and assess and predict the safety condition of the overall system of pressurized components of power station boilers. [ABSTRACT FROM AUTHOR]

Diagnostics of power and energy systems is obviously an important matter. In this paper we present a contribution of using new methodology for the purpose of signal type recognition (for example, faulty/healthy or different types of faults). Our approach uses Bayesian functional data analysis with data depths distributions to detect differing signals. We present our approach for discrimination of pole-to-pole and pole-to-ground short circuits in VSC DC cables. We provide a detailed case study with Monte Carlo analysis. Our results show potential for applications in diagnostics under uncertainty. [ABSTRACT FROM AUTHOR]

The paper aims to identify the significant heterogeneity of socio-economic rural development in Poland by identifying different types of rural areas and clarifying the existing diversity. This objective requires the following: (1) defining the rural development in Polish conditions, (2) abandoning the urban–rural continuum concept, and (3) conducting an analysis involving data aggregated from the local administrative units (2173 gminas/communes). The approach is exploratory and is limited to two questions elaborating the main problem related to the scale and character of rural variety: What socio-economic types of rural area are found in Poland? How are they distributed spatially? The statistical procedure is based on Diday's dynamic clouds typological analysis. This yielded seven types of rural areas that exhaust their diversity. The main indicator of the character of the different types is related to the level of deagrarianisation of the local economy. The authors argue that the a priori urban–rural continuum model should be abandoned in favour of a more open approach. Typologies based on local administrative unit data, which avoid a priori assumptions concerning rurality, provide an excellent insight into the heterogeneity of rural areas. [ABSTRACT FROM AUTHOR]

A successful transformation of the energy and transportation sector is one of the main targets for our society today. Battery electric vehicles can play a key role in future renewable-based energy supply systems because of their ability to store electrical power. Additionally, they provide significant charging flexibility due to the long parking durations. In this paper, we provide insights into the temporal and power-specific flexibility behavior of three different vehicle fleets. These fleets are pool vehicles of office employees, a public authority, and a logistics company. Several parameters, such as the average charging power per charging event or the average plug-in duration per charging event, are discussed. Additionally, we investigate different charging rates and their impact on the temporal flexibility of the charging events. The data analysis shows that the logistics site has the most homogeneous charging profile as well as high charging flexibility, in contrast to the office and public agency site. The results are of significant importance for future applications in the field of smart charging and ancillary services provision. [ABSTRACT FROM AUTHOR]

The accuracy of multibeam echosounder bathymetric measurement depends on the accuracy of the data of the sound speed layers within the water column. This is necessary for the correct modeling of ray bending. It is assumed that the sound speed layers are horizontal and static, according to the sound speed profile traditionally used in the depth calculation. In fact, the boundaries between varying water masses can be curved and oscillate. It is difficult to assess the parameters of these movements based on the sparse sampling of sound velocity profiles (SVP) collected through a survey; thus, alternative or augmented methods are needed to obtain information about water mass stratification for the time of a particular ping or a series of pings. The process of water column data collection and analysis is presented in this paper. The proposed method updates the sound speed profile by the automated detection of varying water mass boundaries, giving the option to adjust the SVP for each beam separately. This can increase the overall accuracy of a bathymetric survey and provide additional oceanographic data about the study area. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth's orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The CSI data of each group are divided into bins of magnitudes and the transition probabilities among the bins are identified to deliver a Markov Chain (MC) model to track the intraday weather condition variations. The proposed model is tested on the measurements of two PV systems located at two different climatic regions: (a) Yverdon-les-Bains, Switzerland; and (b) Oahu, Hawaii, USA. The model is compared with a previously published N-state MC model and the performance of the proposed model is elaborated. [ABSTRACT FROM AUTHOR]