Resource intensity is a measure of the resources needed for the production, processing and disposal of good or services. Its level decides on the costs the companies have to bear both for production and for environmental protection, which in turn have a crucial importance for their competitiveness. Given these facts, our study analyses the issues of resource intensity in the Polish steel industry in correlation to investments made, and more specifically, to the impact of investments on the consumption of energy media used during steel production. Its key element is the development of econometric models presenting the impact of investments on resource consumption in steel production in Poland. Electricity and coke consumption were analysed according to manufacturing installation. The research was carried out on the basis of statistical data for the period of 2004−2018. The obtained findings confirmed the impact of the increase in investment on the decrease in the resource intensity in s... [more]

When detecting the presence of partial discharge (PD) activity in the insulation system in high-voltage equipment, the excitation voltages at variable frequency have been widely used instead of power-frequency (50/60 Hz) sinusoidal voltage in order to reduce the charging power. This work reviews the relevant research on PD activity at very low frequency (VLF) method, including sinusoidal or cosine-rectangular voltage shape, and damped AC (DAC) method. Based on the research history and development status, some major PD characteristics, such as PD inception voltage (PDIV), PD amplitude, PD charge, PD phase-resolved pattern, and several hot issues, such as surface charge decay and statistical time lag, have been discussed. Moreover, the advantages, disadvantages, and applied conditions of two reviewed methods has been summarized. Finally, the prospects have been made on the main development trends of this research field in the future.

The phenomenon of knock is an abnormal combustion occurring in spark-ignition (SI) engines and forms a barrier that prevents an increase in thermal efficiency while simultaneously reducing CO2 emissions. Since knocking combustion is highly stochastic, a cyclic analysis of in-cylinder pressure is necessary. In this study we propose an approach for efficient and robust detection and identification of knocking combustion in three different internal combustion engines. The proposed methodology includes a signal processing technique, called continuous wavelet transformation (CWT), which provides a simultaneous analysis of the in-cylinder pressure traces in the time and frequency domains with coefficients. These coefficients serve as input for a convolutional neural network (CNN) which extracts distinctive features and performs an image recognition task in order to distinguish between non-knock and knock. The results revealed the following: (i) The CWT delivered a stable and effective featur... [more]

Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also,... [more]

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data o... [more]

Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heat... [more]

Frequency response analysis is a method used in transformer diagnostics for the detection of mechanical faults or short-circuits in windings. The interpretation of test results is often performed with the application of numerical indices. However, usually these indices are used for the whole frequency range of the recorded data, returning a single number. Such an approach is inaccurate and may lead to mistakes in the interpretation. An alternative quality assessment is based on the estimation of the local values of the quality index with the moving window method. In this paper, the authors analyse the influence of the width of the input data window for four numerical indices. The analysis is based on the data measured on the transformer with deformations introduced into the winding and also for a 10 MVA transformer measured under industrial conditions. For the first unit the analysis is performed for various window widths and for various extents of the deformation, while in the case of... [more]

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then conver... [more]

Due to the rapidly increasing energy demand and the more serious environmental pollution problems, lithium-ion battery is more and more widely used as high-efficiency clean energy. State of Charge (SOC) representing the physical quantity of battery remaining energy is the most critical factor to ensure the stability and safety of lithium-ion battery. The novelty SOC estimation model, which is two recurrent neural networks with gated recurrent units combined with Coulomb counting method is proposed in this paper. The estimation model not only takes voltage, current, and temperature as input feature but also takes into account the influence of battery degradation process, including charging and discharging times, as well as the last discharge charge. The SOC of the battery is estimated by the network under three different working conditions, and the results show that the average error of the proposed neural network is less than 3%. Compared with other neural network structures, the propo... [more]

A fault propagates along physical paths until it reaches the boundary of the equipment or system, which shows as a functional failure. Hence, inferring the fault propagation helps to ensure the normal operation of the industrial system. To infer the fault propagation in the steam turbine system, a graph model is developed. Firstly, a process graph topology is constructed according to the system mechanism, whose nodes and edges represent the equipment and mutual relationships. Meanwhile, a fault graph topology is built, in which nodes indicate potential faults and edges are inferred propagation paths. Then, the representations of fault nodes are realized through a graph neural network. Lastly, link prediction methods based on nodes’ representations are conducted, along with the paths inference results. Consequently, the accuracy of fault propagation inference for the steam turbine system is over 86%.

In this study, coconut oils have been transesterified with ethanol using microwave technology. The product obtained (biodiesel and FAEE) was then fractional distillated under vacuum to collect bio-kerosene or bio-jet fuel, which is a renewable fuel to operate a gas turbine engine. This process was modeled using RSM and ANN for optimization purposes. The developed models were proved to be reliable and accurate through different statistical tests and the results showed that ANN modeling was better than RSM. Based on the study, the optimum bio-jet fuel production yield of 74.45 wt% could be achieved with an ethanol−oil molar ratio of 9.25:1 under microwave irradiation with a power of 163.69 W for 12.66 min. This predicted value was obtained from the ANN model that has been optimized with ACO. Besides that, the sensitivity analysis indicated that microwave power offers a dominant impact on the results, followed by the reaction time and lastly ethanol−oil molar ratio. The properties of the... [more]

Nowadays we can observe a growing demand for installations of new gas pipelines in Europe. A large number of them are installed using trenchless Horizontal Directional Drilling (HDD) technology. The aim of this work was to develop and compare new machine learning models dedicated for risk assessment in HDD projects. The data from 133 HDD projects from eight countries of the world were gathered, profiled, and preprocessed. Three machine learning models, logistic regression, random forests, and Artificial Neural Network (ANN), were developed to predict the overall HDD project outcome (failure free installation or installation likely to fail), and the occurrence of identified unwanted events. The best performance in terms of recall and accuracy was achieved for the developed ANN model, which proved to be efficient, fast and robust in predicting risks in HDD projects. Machine learning applications in the proposed models enabled eliminating the involvement of a group of experts in the risk... [more]

Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on th... [more]

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg−Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from th... [more]

The dynamic development of transport in recent decades reflects the level of economic development in the world. The transport sector today is one of the main barriers to the achievement of the European Union’s climate protection objectives. More and more restrictive legal regulations define permissible emission limits for the amounts of toxic substances emitted into the atmosphere. Numerical CO2 modeling tools are one way to replace costly on-road testing. Driving cycles, which are an approximation of the vehicle’s on-road operating conditions, are the basis of any vehicle approval procedure. The paper presents a computer tool that uses neural networks to simulate driving tests. Data obtained from tests on the Mercedes E350 chassis dynamometer were used for the construction of the neural model. All the collected operational parameters of the vehicle, which are the input data for the built model, were used to create simulation control runs for driving tests: Environmental Protection Age... [more]

Residential electricity load data can include numerous types of bad data, even clustered bad data, as they that are typically captured by simple measurement instruments. For example, in the case of a time-series of Not-a-Number (NaN) errors, the values before or next to a NaN may appear as the sum of actual values during the times of the NaN series. To utilize load data that includes such erroneous data for prediction or data mining analysis, customized detection and imputation should be conducted. This study proposes a new joint detection and imputation method for handling clustered bad data in residential electricity loads. Examples of these data are known invalid data points, such as consecutive NaN or zero values followed by or being ahead of an outlier. The proposed joint detection and imputation scheme first investigates the neighbors of the invalid data points, using probabilistic forecasting techniques. These techniques are implemented by the next valid neighbors to determine w... [more]

The paper depicts research concerning the value of the electric component of the electromagnetic (EM) energy determined by the NHT3DL meter by Microrad with the 01E measuring probe during a number of flights made by Aero AT-3 R100, Cessna C172, and Tecnam P2006T fixed wing aircrafts and a Robinson R44 Raven helicopter. The point of reference for the recorded measurement was the normative limits of the electromagnetic field (EMF), which can influence a pilot in the course of a flight. Selected studies of the maximum value recorded by the meter was E = 10.66 V/m when landing at an airfield equipped with the VHF (Very High Frequency) omnidirectional radio range (VOR) approach system. Particular attention has been paid to changes in electric field intensity during the operation and their effects on the type of radio navigation systems as well as communication with the airfield control tower. The obtained results were validated in the Statistica 13.3 software for the purpose of a detailed s... [more]

This paper introduces an off-grid DoA estimation via two-stage cascaded network which can resolve a mismatch between true direction-of-arrival (DoA) and discrete angular grid. In the first-stage network, the initial DoAs are estimated with a convolutional neural network (CNN), where initial DoAs are mapped on the discrete angular grid. To deal with the mismatch between initially estimated DoAs and true DoAs, the second-stage network estimates a tuning vector which represents the difference between true DoAs and nearest discrete angles. By using tuning vector, the final DoAs are estimated by moving initially estimated DoAs as much as the difference between true DoAs and nearest discrete angles. The limitation on estimation accuracy induced by the discrete angular grid can be resolved with the proposed two-stage network so that the estimation accuracy can be further enhanced. Simulation results show that adding the second-stage network after the first-stage network helps improve the esti... [more]

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increa... [more]

The need for more effective defence systems is of critical importance because of the rising risk of explosive attacks. Sandwich panels are used as plastically deforming sacrificial structures, absorbing blast wave energy. To the authors’ knowledge, the blast behaviour of sandwich panels with connected (welded/bolted/riveted) corrugated layers has been well covered in literature. Hence, the aim of this numerical study was to develop new, easy-to-build, non-expensive, graded sandwich panel with ‘unconnected’ corrugated layers that can be used as a multipurpose sacrificial protective structure against wide range of blast threats. The proposed sandwich panel is composed of six unconnected aluminium (AL6063-T4) core layers encased in a steel (Weldox 460E) frame with 330 × 330 × 150 mm overall dimensions. The numerical analysis was conducted using Abaqus/Explicit solver. First, the performance of four different nongraded layer topologies (trapezoidal, triangular, sinusoidal, and rectangular)... [more]

Today, Formula 1 race cars are equipped with complex hybrid electric powertrains that display significant cross-couplings between the internal combustion engine and the electrical energy recovery system. Given that a large number of these phenomena are strongly engine-speed dependent, not only the energy management but also the gearshift strategy significantly influence the achievable lap time for a given fuel and battery budget. Therefore, in this paper we propose a detailed low-level mathematical model of the Formula 1 powertrain suited for numerical optimization, and solve the time-optimal control problem in a computationally efficient way. First, we describe the powertrain dynamics by means of first principle modeling approaches and neural network techniques, with a strong focus on the low-level actuation of the internal combustion engine and its coupling with the energy recovery system. Next, we relax the integer decision variable related to the gearbox by applying outer convexifi... [more]

In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The... [more]

Power system steady-state security relates to its robustness under a normal state as well as to withstanding foreseeable contingencies without interruption to customer service. In this study, a novel cellular computation network (CCN) and hierarchical cellular rule-based fuzzy system (HCRFS) based online situation awareness method regarding steady-state security was proposed. A CCN-based two-layer mechanism was applied for voltage and active power flow prediction. HCRFS block was applied after the CCN prediction block to generate the security level of the power system. The security status of the power system was visualized online through a geographic two-dimensional visualization mechanism for voltage magnitude and load flow. In order to test the performance of the proposed method, three types of neural networks were embedded in CCN cells successively to analyze the characteristics of the proposed methodology under white noise simulated small disturbance and single contingency. Results... [more]

The number of wind farms is increasing every year because many countries are turning their attention to renewable energy sources. Wind turbines are considered one of the best alternatives to produce clean energy. Most of the wind farms installed supervisory control and data acquisition (SCADA) system in their turbines to monitor wind turbines and logged the information as time-series data. It demands a powerful information extraction process for analysis and prediction. In this research, we present a data analysis framework to visualize the collected data from the SCADA system and recurrent neural network-based variant long short-term memory (LSTM) based prediction. The data analysis is presented in cartesian, polar, and cylindrical coordinates to understand the wind and energy generation relationship. The four features: wind speed, direction, generated active power, and theoretical power are predicted and compared with state-of-the-art methods. The obtained results confirm the applica... [more]