Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy (R2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy (Cv(RMSE)) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently.

Due to environmental concerns and increased energy demand, natural esters are among the alternatives to mineral oils in transformers. This study examines the electrical behavior of natural ester-based ZnO nanofluids at different concentrations in the range of 0.05−0.4 g/L. AC breakdown voltages are measured in a horizontally positioned sphere−sphere electrode system according to IEC 60156 specifications. The measurement data are analyzed using Weibull and normal distribution functions. Breakdown voltages with 1%, 10% and 50% probability are also estimated, these probabilities being of great interest for the design of power electrical components. Experimental results show that AC breakdown voltage increases with the concentration of ZnO nanoparticles, except for the concentration of 0.05 and 0.4 g/L of ZnO. Moreover, breakdown voltages at 1% and 10% probability increase by 22.7% and 13.2% when adding 0.1 g/L ZnO to natural ester, respectively.

Reservoir and pumped hydro storage facilities represent one of the best options for providing flexibility at low marginal cost and very low life cycle carbon emissions. However, hydropower generation is subject to physical, environmental and regulatory constraints, which introduce complexity in the modelling of hydropower in the context of transition energy analysis. In this article, a probabilistic model for hydropower generation is developed in order to improve an hourly-resolved tool for transition path analysis presented in previous research. The model is based on time series analysis, which exploits the fact that the different constraints affecting hydropower generation were met in the past. The upgraded version of the transition path analysis tool shows a decrease in the hydropower flexibility as compared with previous published results, providing a better picture of the benefits and drawbacks associated with a specific transition path under analysis, for example in terms of asse... [more]

This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from th... [more]

A smart monitoring system capable of detecting and classifying the health conditions of MV (Medium Voltage) underground cables is presented in this work. Using the analysis technique proposed here, it is possible to prevent the occurrence of catastrophic failures in medium voltage underground lines, for which it is generally difficult to realize maintenance operations and carry out punctual inspections. This prognostic method is based on Frequency Response Analysis (FRA) and can be used online during normal network operation, resulting in a minimally invasive tool. In order to obtain the good results shown in the simulation section, it is necessary to develop a lamped equivalent circuit of the network branch under consideration. The standard π-model is used in this paper to analyse sections of a medium voltage cable and the parameter variations with temperature are used to classify the state of health of the line. In fact, the variation of the electrical parameters produces a correspon... [more]

This paper presents a review of the existing models for the estimation of explosion-induced crushed and cracked zones. The control of these zones is of utmost importance in the rock explosion design, since it aims at optimizing the fragmentation and, as a result, minimizing the fine grain production and recovery cycle. Moreover, this optimization can reduce the damage beyond the set border and align the excavation plan with the geometric design. The models are categorized into three groups based on the approach, i.e., analytical, numerical, and experimental approaches, and for each group, the relevant studies are classified and presented in a comprehensive manner. More specifically, in the analytical methods, the assumptions and results are described and discussed in order to provide a useful reference to judge the applicability of each model. Considering the numerical models, all commonly-used algorithms along with the simulation details and the influential parameters are reported and... [more]

Large rotating machinery, such as centrifugal gas compressors and pumps, have been widely applied and acted as crucial components in the oil and gas industries. Breakdowns or deteriorated performance of these rotating machines can bring significant economic loss to the companies. In order to conduct effective maintenance and avoid unplanned downtime, a system-wide health indicator is proposed in this paper. The health indicator not only uses a dynamic risk profile, but also considers financial loss and the fault probability based on condition monitoring data. This methodology is carried out by four steps: fault detection, probability of fault calculation, consequence of fault calculation and dynamic risk assessment. In our methodology, the fault probability is calculated by robust Mahalanobis distance, presenting as a system-wide feature from a sparse autoencoder fault detection model enabled early fault detection. The value of the health indicator is presented in financial loss, which... [more]

A main challenge towards ensuring improved lifetime performance and reduction of financial risks of photovoltaic (PV) technologies remains the accurate degradation quantification of field systems and the dependency of this performance loss rate to climatic conditions. The purpose of this study is to address these technological issues by presenting a unified methodology for accurately calculating the degradation rate (RD) of PV systems and provide evidence that degradation mechanisms are location dependent. The method followed included the application of data inference and time series analytics, in the scope of comparing the long-term RD of different crystalline Silicon (c-Si) PV systems, installed at different climatic locations. The application of data quality and filtering steps ensured data fidelity for the RD analysis. The yearly RD results demonstrated that the adopted time series analytical techniques converged after 7 years and were in close agreement to the degradation results... [more]

Small hydropower (SHP) plays an important role in the Baltic States as a reliable and efficient source of electricity from renewable sources. This study presents the historical development, current status, and possible trends for the future development of SHP in the Baltic States with insights into the legal background and development policies for SHP in Estonia, Latvia and Lithuania. For the assessment of hydraulic structures in the Baltic States, the historical data was used supplemented with data from hydropower associations, the national departments of statistics, electricity transmission systems operators, etc. The currently recommended best practice for SHP development is the utilisation of existing sites with available infrastructure. These include old water mill sites and existing dam sites, which, in the Baltic States, number more than 1500. The majority of these sites have their power potential attributed to micro-hydro (<100 kW). In this study the potential of the hydropo... [more]

Power and communication lines with round wires are often used in electrical engineering. The skin and proximity effects affect the current density distribution and increase resistances and energy losses. Many approaches were proposed to calculate the effects and related quantities. One of the simplest approximate closed solutions neglects the dimensions of neighboring wires. In this paper, a solution to this problem is proposed based on the method of successive reactions. In this context, the solution with substitutive filaments is considered as the first approximation of the true solution. Several typical arrangements of wires in single-phase communication lines or three-phase bus ducts are considered to detect the limits of applicability of the first approximation. The error of the first approximation grows with wire radius to skin depth ratio and wire radius to wire spacing ratio. When the wire radius to skin depth ratio is up to 1, and the gap between the wires is above the wire ra... [more]

Monitoring and management of water levels has become an essential task in obtaining hydroelectric power. Activities such as water resources planning, supply basin management and flood forecasting are mediated and defined through its monitoring. Measurements, performed by sensors installed on the river facilities, are used for precisely information about water level estimations. Since weather conditions influence the results obtained by these sensors, it is necessary to have redundant approaches in order to maintain the high accuracy of the measured values. Staff gauge monitored by conventional cameras is a common redundancy method to keep track of the measurements. However, this method has low accuracy and is not reliable once it is monitored by human eyes. This work proposes to automate this process by using image processing methods of the staff gauge to measure and deep neural network to estimate the water level. To that end, three models of neural networks were compared: the residua... [more]

Three-dimensional (3D) reservoir geological modeling is an advanced reservoir characterization method, which runs through the exploration and the development process of oil and gas fields. Reservoir geological modeling is playing an increasingly significant role in determining the distribution, internal configuration, and quality of a reservoir as well. Conventional variogram-based methods such as statistical interpolation and reservoir geological modeling have difficulty characterizing complex reservoir geometries and heterogeneous reservoir properties. Taking advantage of deep feedforward neural networks (DFNNs) in nonlinear fitting, this paper compares the reservoir geological modeling results of different methods on the basis of an existing lithofacies model and seismic data from the X area of Karamay, Xinjiang, China. Adopted reservoir geological modeling methods include conventional sequential Gaussian simulation and DFNN-based reservoir geological modeling method. The constraine... [more]

Buildings are among the main protagonists of the world’s growing energy consumption, employing up to 45%. Wide efforts have been directed to improve energy saving and reduce environmental impacts to attempt to address the objectives fixed by policymakers in the past years. Meanwhile, new approaches using Machine Learning regression models surged in the modeling and simulation research context. This research develops and proposes an innovative data-driven black box predictive model for estimating in a dynamic way the interior temperature of a building. Therefore, the rationale behind the approach has been chosen based on two steps. First, an investigation of the extant literature on the methods to be considered for tests has been conducted, shrinking the field of investigation to non-recursive multi-step approaches. Second, the results obtained on a pilot case using various Machine Learning regression models in the multi-step approach have been assessed, leading to the choice of the Sup... [more]

The paper introduces the artificial intelligence (AI) approach for modeling fluidized adsorption beds. The idea of fluidized bed application allows a significantly increased heat transfer coefficient between adsorption bed and the surface of a heat exchanger, improving the performance of adsorption cooling and desalination systems. The Long Short-Term Memory (LSTM) network algorithm was used, classified as a deep learning method, to predict the vapor mass quantity in the adsorption bed. The research used an LSTM network with two hidden layers. The network used in the study is composed of seven inputs (absolute pressures in the adsorption chamber and evaporator, the temperatures in adsorption chamber and evaporator, relative pressure, the temperatures in the center of adsorption bed and 25 mm from the bed center, the kind of the solids mixture, the percentage value of the addition) and one output (mass of the sorption bed). The paper presents numerical research concerning mass predictio... [more]

The effects of on-road disturbances on the aerodynamic drag are attracting attention in order to accurately evaluate the fuel efficiency of an automobile on a road. The present study investigated the effects of cornering motion on automobile aerodynamics, especially focusing on the aerodynamic drag. Using a towing tank facility, measurements of the fluid-dynamic force acting on Ahmed models during steady-state cornering were conducted in water. The investigation included Ahmed models with slant angles θ = 25° and 35°, reproducing the wake structures of two different types of automobiles. The drag increase due to steady-state cornering motion was experimentally measured, and showed good agreement with previous numerical research, with the measurements conducted at a Reynolds number of 6 × 105, based on the model length. The Ahmed model with θ = 35° showed a greater drag increase due to the steady-state cornering motion than that with θ = 25°, and it reached 15% of the total drag at a co... [more]

Potential leakage of reservoir fluids is considered a key risk factor for geologic CO2 sequestration (GCS), with concerns of their chemical impacts on the quality of overlying underground sources of drinking water (USDWs). Effective risk assessment provides useful information to guide GCS activities for protecting USDWs. In this study, we present a quantified risk assessment case study of an active commercial-scale CO2-enhanced oil recovery (CO2-EOR) and sequestration field, the Farnsworth Unit (FWU). Specific objectives of this study include: (1) to quantify potential risks of CO2 and brine leakage to the overlying USDW quality with response surface methodology (RSM); and (2) to identify water chemistry indicators for early detection criteria. Results suggest that trace metals (e.g., arsenic and selenium) are less likely to become a risk due to their adsorption onto clay minerals; no-impact thresholds based on site monitoring data could be a preferable reference for early groundwater... [more]

The prediction of phase transformation of biomass ashes is challenging due to the highly variable composition of these fuels as well as the complex processes accompanying phase transformations. The AFT (Ash Fusion Temperature) model was performed in Statistica 13.1 software. This model was divided into three separate submodels, which were designed to predict the characteristic ash melting temperatures for raw and modified biomass. It is based on the chemical composition of fuel and ash as obtained using ash analysis standards. For the discussed models, several coefficients describing multiple regression parameters are presented. The AFT model discussed in this article is suitable for predicting ash fusion temperatures for biomass and allows for the prediction of the temperature with an average error of <±70.05 °C for IDT; <±51.98 °C for HT; <±47.52 °C for FT for raw biomass. For some of the additionally tested biomass, a value higher than the average difference between the mea... [more]

The main point of this paper was to perform the design process for and verify the properties of an adaptive neural controller implemented for a real nonlinear object—an electric drive with an Induction Motor (IM). The controller was composed as a parallel combination of the classical Proportional-Integral (PI) structure, and the second part was based on Radial Basis Function Neural Networks (RBFNNs) with the on-line recalculation of the weight layer. The algorithm for the adaptive element of the speed controller contained two parts in parallel. The first of them was dedicated for the main path of the neural network calculations. The second realized the equations of the adaptation law. The stability of the control system was provided according to the Lyapunov theorem. However, one of the main issues described in this work is the optimization of the constant part of the analyzed parallel speed controller. For this purpose, the Grey Wolf Optimizer (GWO) was applied. A deep analysis of the... [more]

Gas hydrate saturation is an important index for evaluating gas hydrate reservoirs, and well logs are an effective method for estimating gas hydrate saturation. To use well logs better to estimate gas hydrate saturation, and to establish the deep internal connections and laws of the data, we propose a method of using deep learning technology to estimate gas hydrate saturation from well logs. Considering that well logs have sequential characteristics, we used the long short-term memory (LSTM) recurrent neural network to predict the gas hydrate saturation from the well logs of two sites in the Shenhu area, South China Sea. By constructing an LSTM recurrent layer and two fully connected layers at one site, we used resistivity and acoustic velocity logs that were sensitive to gas hydrate as input. We used the gas hydrate saturation calculated by the chloride concentration of the pore water as output to train the LSTM network. We achieved a good training result. Applying the trained LSTM re... [more]

This paper is devoted to an analytical approach to the magnetoacoustic tomography with magnetic induction (MAT-MI) problem for three-layer low-conductivity objects. For each layer, we determined closed-form analytical expressions for the eddy current density and Lorentz force vectors based on the separation of variables method. Next, the analytical formulas were validated with numerical solutions obtained with the help of the finite element method (FEM). Based on the acoustic dipole radiation theory, the influence of the transducer reception pattern on MAT-MI was investigated. To obtain acoustic wave patterns, as a system transfer function we proposed the Morlet wavelet. Finally, image reconstruction examples for objects of more complex shapes are presented, and the influence of the MAT-MI scanning resolution and the presence of the noise on the image reconstruction quality was studied in detail.

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the La... [more]

The wind power ramp event includes large fluctuations in wind power within a short period of time. To maintain grid stability, defining, identifying, and predicting the wind power ramp event is inevitable. Therefore, a comprehensive assessment method of wind power ramp events that combines the generalized information of the source, grid, and load sides is proposed. In this method, we put forward a channel self-selected multi-layer coefficient correction model (CSMCC) and wind power ramp threshold, according to the allowable value of a grid frequency change. Additionally, the availability of data-driven modeling methods is verified by performing autocorrelation analysis. Finally, the comprehensive evaluation method, which combines the back propagation (BP) neural network, supports the vector machine and CSMCC model is proved to be effective. This paper has a certain reference significance for basic research on large-scale wind power safety and efficient utilization.

The purpose of this paper is to investigate experimentally the influence of the cavitation extent on the pressure and velocity fluctuations in a small convergent−divergent channel. The mean cavity length is determined from high-speed photography images. The mean pressure and the intensity of the pressure fluctuations are obtained from the transient pressure signals recorded by two pressure transducers at the inlet and outlet of the test section. The statistical turbulence quantities are derived from the instantaneous velocity fields measured by the laser-induced fluorescent particle image velocimetry (PIV-LIF) technique. The experimental results show that the decrease of the cavitation number (the increase in the extent of cavitation) leads to a rise in the turbulent fluctuations in the wake region due to the impact of vapour clouds collapsing, while the presence of a vapour phase is found to reduce the streamwise and cross-stream velocity fluctuations in the attached cavity. It might... [more]

Shale oil reservoirs are characterized by complex lithology, complex mineral composition and strong heterogeneity. This causes great difficulty in lithologic evaluation. In this paper, a method of lithology identification is proposed by means of intersection plot method and machine learning method, and lithology evaluation is carried out by combining the calculation of mineral content with a multi-mineral optimization model. The logging response characteristics of five lithologies are analyzed by using the logging curves selected by principal component analysis (PCA) discriminant analysis. In lithology identification, the system clustering algorithm is selected to identify shale oil reservoir lithology through layer-by-layer subdivision of sample lithology classification. Logging data has high vertical resolution and good continuity, and mineral prediction using logging data can ensure high accuracy. In this paper, the method of calculating mineral content by using multi-mineral optimi... [more]

With the uncertainty concerning the future use of natural resources due to depletion and lack of access caused by the pandemic and recent political events that led to increased prices, nuclear energy may become an alternative efficient energy. NPPs raise serious concerns, including waste management, and any case of an NPP accident has the potential to disrupt the positive impact of energy production in terms of circular economies. Our research analyzed the impact of nuclear incidents as examples of disasters worldwide to decide whether any of the different forms of insurance coverage could be useful in future events. By using 2533 historical records of incidents from 1901 to June 2022, we set out to find the best predictor of damage causes and further observe whether the validation of current forms of insurance may be possible. The disaster subtype and declaration represent the best predictor of the total damage value (adjusted or not) for all types of disasters, including nuclear. The... [more]