Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries ge... [more]

This paper deals with a method of quantifying the harmonic contribution of each harmonic source to system voltage distortion. Assessing the harmonic contribution of individual harmonic sources is essential for mitigating and managing system harmonic levels. Harmonic contributions can be evaluated using the principle of voltage superposition with equivalent voltage models for harmonic sources. In general, the parameters of equivalent voltage models are estimated numerically because it is difficult to measure them directly. In this paper, we present an effective method for estimating equivalent model parameters based on the random sample consensus (RANSAC) and recursive least square (RLS) with a variable forgetting factor. The procedure for quantifying harmonic contributions using equivalent models is also introduced. Additionally, we propose a network diagram of harmonic contributions that makes it easy to understand the harmonic distortion contributions of all harmonic sources.

One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network... [more]

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the... [more]

Most temperature predictions for deep geothermal applications rely on correcting bottom-hole temperatures (BHTs) to undisturbed or static formation temperatures (SFTs). The data used for BHT correction are usually of low quality due to a lack of information and poor documentation, and the uncertainty of the corrected SFT is therefore unknown. It is supposed that the error within the input data exceeds the error due to the uncertainty of the different correction schemes. To verify this, we combined a global sensitivity study with Sobol indices of six easy-to-use conventional correction schemes of the BHT data set of the Bavarian Molasse Basin with an uncertainty study and developed a workflow that aims at presenting a valid error range of the corrected SFTs depending on the quality of their input data. The results give an indication of which of the investigated correction methods should be used depending on the input data, as well as show that the unknown error in the input parameters e... [more]

Conventional fuels commonly used in cars with combustion engines and the effects of their combustion have a very negative impact on the state of the environment. The combustion of liquid fuels causes the introduction of many thousands of tons of CO2 and other harmful substances into the atmosphere every year. That is why the authorities of many countries are introducing more and more stringent emission standards for cars with internal combustion engines, and car manufacturers are trying to meet these standards. Therefore, the aim of the undertaken research was to compile and analyze the power of spark engines in individual capacity ranges, compression ratios, efficiency, CO2 emissions, dependence of combustion on engine capacity, dependence of CO2 emissions on engine capacity, and dependence of combustion on engine power. The conducted research also compared the level of average selected variables related to CO2 emission in terms of engine displacement by country of production using st... [more]

Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A 2×2 experimental PV array shows... [more]

Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energ... [more]

In order to study the effect of tip clearance on the internal cavitation stability of a shunt blade inducer, an external characteristics experiment of a centrifugal pump with a shunt blade inducer was carried out. Based on the turbulence model and mixture model, the cavitating flow in a centrifugal pump with the inducer was numerically simulated. The influence of tip clearance on the cavitating flow in a shunt inducer was studied and analyzed. Through the research, it was found that tip clearance has a certain influence on the critical cavitation coefficient. The existence of the tip clearance caused a significant leakage vortex near the inducer’s inlet and a strong transient effect was shown. The location and degree of cavitation caused by the tip leakage are clarified in this paper. Tip clearance has a great impact on the pressure distribution on a shunt blade inducer. The influence law of tip clearance on an inducer’s blade load distribution was clarified. The results showed that ti... [more]

Nowadays, the mineral resources formed by geological processes have been effectively utilized with the boom exploration of novel technologies. Traditional analytical methods, such as X-ray Fluorescence, X-ray diffraction, and Scanning electron microscopy, remain the commonly used approaches for resource detection. However, recent accelerations in terahertz component progress have promoted researchers to discover more potential technologies in mineral resource exploration. In this article, the various porosities and calcination products of Chlorite mica carbonate schist, a mineral resource and potent medicine, are detected using the terahertz time−domain spectroscopy. The terahertz constant measurement of Chlorite mica carbonate schist tablets including the amplitude and phase values was carried out. After Fourier transforms, notable differences of absorption coefficients and refractive index are observed from these experimental samples, which have compelling indications to quantitative... [more]

Forecasting has extreme importance in industry due to the numerous competitive advantages that it provides, allowing to foresee what might happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset’s behaviour several days in advance.

In underground mining, new workings (tunnels) are constructed by blasting or mechanical excavation. The blasting technique used in underground mines is supported by economic aspects, especially for deposits characterized by hard rocks. Unfortunately, the quality of the result may be different than expected in terms of the general geometry of work or the roughness of excavation surfaces. The blasting technique is also a source of vibrations that may affect other existing structures, affecting their stability. Therefore, it is of great importance to monitor both the quality of the new tunnels and changes in existing tunnels that may cause rockfall from the sidewalls and ceilings of both new and existing tunnels. The length of mining tunnels and support structures in underground mines is massive. Even if one would like to limit monitoring of tunnel geometry to those used every day for major technological processes such as transport, it is a vast amount of work. What is more, any stationar... [more]

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyz... [more]

Deterioration trend prediction of hydropower units helps to detect abnormal conditions of hydropower units and can prevent early failures. The reliability and accuracy of the prediction results are crucial to ensure the safe operation of the units and promote the stable operation of the power system. In this paper, the long short-term neural network (LSTM) is introduced, a comprehensive deterioration index (CDI) trend prediction model based on the time−frequency domain is proposed, and the prediction accuracy of the situation trend of hydropower units is improved. Firstly, the time−domain health model (THM) is constructed with back-propagation neural network (BPNN) and condition parameters of active power, guide vane opening and blade opening and the time−domain indicators. Subsequently, a frequency-domain health model (FHM) is established based on ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn), and k-means clustering algorithm. Later, the time−domain degradat... [more]

In this paper, a hybrid PI radial basis function neural network (RBFNN) controller is used for a plant with significant disturbances related to the mechanical part of the construction. It is represented through a two-mass system. State variables contain additional components—as a result, oscillations affect the precision of control. Classical solutions lead to movements of the poles of the whole control structure. However, proper tuning of the controller needs detailed identification of the object. In this work, the neural network is implemented to improve the classical PI controller’s performance and mitigate the errors generated by oscillations of the mechanical variables and parametric uncertainties. The proposed control strategy also guarantees the closed-loop stability of the system. The mathematical background is firstly presented. Afterward, the simulation results are shown. It can be stated that the results are very promising, and a significant improvement in oscillations dampi... [more]

Power system demand forecasting is a crucial task in the power system engineering field. This is due to the fact that most system planning and operation activities basically rely on proper forecasting models. Entire power infrastructures are built essentially to provide and serve the consumption of energy. Therefore, it is very necessary to construct robust and efficient predictive models in order to provide accurate load forecasting. In this paper, three techniques are utilized for short-term load forecasting. These techniques are deep neural network (DNN), multilayer perceptron-based artificial neural network (ANN), and decision tree-based prediction (DR). New predictive variables are included to enhance the overall forecasting and handle the difficulties caused by some categorical predictors. The comparison among these three techniques is executed based on coefficients of determination R2 and mean absolute error (MAE). Statistical tests are performed in order to verify the results a... [more]

A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10−8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and imp... [more]

In this paper, a study was completed investigating the financial viability of a 5 MW solar power plant in Montenegro with direct access to the market, rather than a long-term power purchase agreement. The empirical research included an econometric analysis and forecast of the prices on the exchange market, using two methods, autoregressive integrated moving average (ARIMA) and neural network auto regression (NNAR), which are compared to the forecast electricity prices. The former was used in order to obtain the electricity prices forecast, since it showed significantly better predictive performances. Consequently, the financial analysis results indicated this business strategy is a financially more viable option, even though it implies increased risks. All investigated metrics and sensitivity analysis pointed in favor of this option, which has significantly higher profitability with a shorter payback period, compared to the usual market strategy. The main conclusion and recommendation... [more]

The automated evaluation of machine conditions is key for efficient maintenance planning. Data-driven methods have proven to enable the automated mapping of complex patterns in sensor data to the health state of a system. However, generalizable approaches for the development of such solutions in the framework of industrial applications are not established yet. In this contribution, a procedure is presented for the development of data-driven condition monitoring solutions for industrial hydraulics using supervised learning and neural networks. The proposed method involves feature extraction as well as feature selection and is applied on simulated data of a hydraulic press. Different steps of the development process are investigated regarding the design options and their efficacy in fault classification tasks. High classification accuracies could be achieved with the presented approach, whereas different faults are shown to require different configurations of the classification models.

Nowadays, cognitive computing has become the popular solution to many problems arising in the energy industry, such as the creation of renewable technologies, energy saving, and searching for new sources. Last decade, a substantial number of scientific papers aiming to support these tasks were published. On the other hand, some years ago, the “cognitive enterprise” (CE) concept was introduced by the IBM company, which assumes, among others, the cognitive technologies used to increase enterprise intelligence. On the road to obtaining the status of a “cognitive enterprise”, it should overcome many challenges. Thus, the aim of the paper was to analyze the current state of research on the application of cognitive computing in the energy industry and to define the trends, challenges, milestones, and perspectives in scientific work’s development. The aim has been achieved using the bibliometric approach. The preliminary analysis was made by Web of Science data sources; 4182 records were retr... [more]

According to the characteristics that the dynamic tension of rock material is elastic brittle and the dynamic compression is elastic plastic, based on previous studies, the influence of initial damage is considered in the established compression damage model, and the calculation formula of the damage threshold used to evaluate whether the surrounding rock is affected by blasting is given. According to the classic rock impact dynamic damage model and statistical damage mechanics theory, a rock compressive and tensile statistical damage constitutive model and impact damage model under blasting load is proposed. Based on the proposed damage model and the classic dynamic tensile damage model, the numerical simulation of blasting damage was carried out, and the numerical calculation results were compared with the field measurement results. Based on the established damage model, to further clarify the damage evolution characteristics of rock under blasting load, fractal dimension theory was... [more]

In this present investigation, emittance and performance attributes of a diesel engine using micro-algae spirulina blended biodiesel mixtures of various concentrations (20%, 35%, 50%, 65%, 80%, and 100%) were evaluated. An optimization model was also developed using an Artificial Neural Network (ANN) to characterize the experimental parameters. Experimental findings demonstrated significant improvement in brake specific fuel consumption (BSFC) using varied blends. Furthermore, brake thermal efficiency (BTE) is decreased gradually for biodiesel blends as compared to diesel. Micro-algae spirulina blends have shown lower concentrations of NOX and HC while increasing CO2 relative to pure diesel. To develop the model, three sets of optimizers, namely, adam, nadam, and adagrad, along with activation functions, such as sigmoid, softmax, and relu, were selected. The results revealed that sigmoid activation function with adam learning optimizer by using 32 hidden layer neurons has given the lea... [more]

Electrostatic energy harvesters convert kinetic energy into electrical energy via variable capacitors. Efforts to improve their power output are hampered by a lack of understanding of the fundamental limit for energy conversion efficiency. In heat engines, the theoretical limit of conversion efficiency is intrinsically related to entropy and the second law of thermodynamics. Laying the foundation for similar concepts for kinetic energy harvesters may be necessary for establishing a conversion efficiency limit. Thus, the mixing entropy concept is borrowed from statistical mechanics and is adapted here, for the first time, to characterize the energy transfer between coupled mechanical−electrical oscillators. The investigated system is composed of a spring-mass coupled to an inductance-capacitor circuit via a variable capacitor. Combining the two subsystems (electrical and mechanical) generates entropy, referred to as mixing entropy. A non-dimensional study of the governing equations of t... [more]

To explore the inlet boundary layer (IBL) influence on the tandem cascade aerodynamic performance, this paper took the high subsonic compressor NACA65 K48 cascade and its modified tandem cascade as the research object. The effects of the IBL thickness and the skewed IBL on the aerodynamic performance of the original cascade and tandem cascade were analyzed based on the numerical method. The results show that the tandem cascade effective design makes it better than the original cascade in the aerodynamic performance under different IBL conditions. Compared with the collateral IBL, the skewed IBL can effectively improve the aerodynamic performance of the original cascade and tandem cascade by suppressing the endwall cross flow, but an increase in the IBL thickness will suppress this advantage. In addition, the increase of incidence angle or the IBL thickness will make the tandem cascade forward blade corner separation more serious and cause the flow passage to be blocked, which seriously... [more]

Asphaltene are large molecular crude constituents and their existence is related to numerous problems. However, nanofluids have proven to be a very stable and effective way of dealing with asphaltene agglomerations. This research addresses the effectiveness of nanofluids as compared to traditional and available (FLOW-X) commercial inhibitors. The synthesis and characterization of two green NPs and the preparation of nanofluids were performed successfully in this study. It was found that by tuning the concentration of nanofluid, the efficiency increases by 17%. Crude samples have shown different responses to nano inhibitors. It was found that nanofluids increase asphaltene dissolution by nearly 22% as compared to commercial inhibitors.