In relation to the situation caused by the pandemic, which may also take place in the future, there is a need to find effective solutions to improve the economic situation of the floristry industry. The production and sale of flowers is time-consuming and long-term. Therefore, any information that causes the impossibility of selling the plants will result in a reduction of profitability or bankruptcy of such companies. Research on rationally utilizing biowaste from plant cultivation as well as unsold flowers for environmental protection and effective use of their potential as a raw material for bioenergy production were examined in this article. The aim of this study was to analyze the energetic potential of the biodegradable fraction of waste from floriculture. The trials included floricultural waste containing the stems, leaves and flowers of different species and hybrid tulips (Tulipa L.), roses (Rosa L.), sunflowers (Helianthus L.) and chrysanthemums (Dendranthema Des Moul.). Their... [more]

The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained res... [more]

The large number of species included in the detailed kinetic mechanisms represents a serious challenge for numerical simulations of reactive flows, as it can lead to large CPU times, even for relatively simple systems. One possible solution to mitigate the computational cost of detailed numerical simulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive Reduced Chemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemical space partitioning for the generation of locally reduced mechanisms, but this task is often challenging because of the high-dimensionality, as well as the high non-linearity associated to reacting systems. Moreover, the importance of this step in the overall approach is not negligible, as it has effects on the mechanisms’ level of chemical reduction and, consequently, on the accuracy and the computational speed-up of the adaptive simulation. In this work, two different clustering algorithms for the pa... [more]

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the auto... [more]

In this article, a description is presented of simulation investigations concerning the quality of regeneration effects of a technical object in an intelligent system with an artificial neural network. All repairable technical objects used are subject to a cyclic (random) process of damages and repairs in the time of their operation. A reduction of the parameters connected with the use of objects is the fundamental feature of this process. This results in the need of a regeneration (technical maintenance) of this object. Regeneration of an object in an intelligent system with an artificial neural network constitutes an effective approach to this problem. The problem of qualitative assessments of a maintenance process organized in this manner is the focus of this article. For this purpose, a program of simulation investigations is presented. The research program consists of a description of the models of the operation processes of technical objects, determination of the input data to th... [more]

The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.

This paper applies a heuristic approach to optimize the predictor variables in artificial neural networks when forecasting raw material prices for energy production (coking coal, natural gas, crude oil and coal) to achieve a better forecast. Two goals are (1) to determine the optimum number of time-delayed terms or past values forming the lagged variables and (2) to improve the forecast accuracy by adding intrinsic signals to the lagged variables. The conclusions clearly are in opposition to the actual scientific literature: when addressing the lagged variable size, the results do not confirm relationships among their size, representativeness and estimation accuracy. It is also possible to verify an important effect of the results on the lagged variable size. Finally, adding the order in the time series of the lagged variables to form the predictor variables improves the forecast accuracy in most cases.

This paper presents an improved estimation strategy for the rotor flux, the rotor speed and the frequency required in the control scheme of a standalone wind energy conversion system based on self-excited three-phase squirrel-cage induction generator with battery storage. At the generator side control, the rotor flux is estimated using an adaptive Kalman filter, and the rotor speed is estimated based on an artificial neural network. This estimation technique enhances the robustness against parametric variations and uncertainties due to the adaptation mechanisms. A vector control scheme is used at the load side converter for controlling the load voltage with respect to amplitude and frequency. The frequency is estimated by a Kalman filter method. The estimation schemes require only voltage and current measurements. A power management system is developed to operate the battery storage in the DC-microgrid based on the wind generation. The control strategy operates under variable wind spee... [more]

Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg−Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using... [more]

A convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the non-convex model of economic dispatch into a convex approach that guarantees the global optimum and has an easy implementation in specialized software, i.e., CVX. This conversion is accomplished by performing a mathematical relaxation to ensure the global optimum in DCMG. The SOCP model includes changeable energy purchase prices in the DCMG operation, which makes it in a suitable formulation to be implemented in real-time operation. An energy short-term forecasting model based on a receding horizon control (RHC) plus an artificial neural network (ANN) is used to forecast primary sources of renewable energy for periods of 0.5h. The proposed mathematical approach is compared to the non-convex... [more]

Anode-supported solid oxide fuel cells (SOFCs) model based on artificial neural network (ANN) and optimized design variables were modeled. The input parameters of the anode-supported SOFC model developed in this study are as follows: current density, temperature, electrolyte thickness, anode thickness, anode porosity, and cathode thickness. Voltage was estimated from the SOFC model with the input parameters. Numerical results show that the SOFC model constructed in this study can represent the actual SOFC characteristics very well. There are four design parameters to be optimized: electrolyte, anode, cathode thickness, and anode porosity. To derive the optimal combination of the design parameters, we have used a multi-armed bandit algorithm (MAB), and developed a methodology for deriving near-optimal parameter set without searching for all possible parameter sets.

The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 w... [more]

The biomass is regarded as a part of renewable energy sources (RES), which can satisfy energy demands. Biomass obtained from plantations is characterized by low bulk density, which increases transport and storage costs. Briquetting is a technology that relies on pressing biomass with the aim of obtaining a denser product (briquettes). In the production of solid biofuels, the technological as well as material variables significantly influence the densification process, and as a result influence the end quality of briquette. This process progresses differently for different materials. Therefore, the optimal selection of process’ parameters is very difficult. It is necessary to use a decision support tool—decision support system (DSS). The purpose of the work was to develop a decision support system that would indicate the optimal parameters for conducting the process of producing Miscanthus and willow briquettes (pre-comminution, milling and briquetting), briquette parameters (durability... [more]

The article is devoted to the problem of reducing fuel consumption in a diesel generator set (DGS) as a part of a wind-diesel power plant (WDPP). The object of the research is a variable speed DGS. The goal is to develop the WDPP intelligent control system, providing an optimal shaft speed of an internal combustion engine (ICE). The basis of the intelligent control system is an economy mode setting device (EMSD), which controls the fuel supply to the ICE. The functional chart of EMSD has been presented. The main EMSD blocks contain a controller and an associative memory block. The associative memory block is a software model of an artificial neural network that determines the optimal shaft speed of the ICE. An algorithm for the WDPP intelligent control system has been developed and tested using the WDPP Simulink model. The EMSD prototype has been created, and its research has been conducted. Dependences of the change in specific and absolute fuel consumption on the load power have been... [more]

The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithm... [more]

Smart Grids (SGs) can be successfully supported by Wireless Sensor Networks (WSNs), especially through these consisting of intelligent sensors, which are able to efficiently process the still growing amount of data. We propose a contribution to the development of such intelligent sensors, which in an advanced version can be equipped with embedded low-power artificial neural networks (ANNs), supporting the analysis and the classification of collected data. This approach allows to reduce the energy consumed by particular sensors during the communication with other nodes of a larger WSN. This in turn, facilitates the maintenance of a net of such sensors, which is a paramount feature in case of their application in SG devices distributed over a large area. In this work, we focus on a novel, energy-efficient neighborhood mechanism (NM) with the neighborhood function (NF). This mechanism belongs to main components of self learning ANNs. We propose a realization of this component as a special... [more]

Advanced thermal control technologies have been continuously developed to complement conventional models and algorithms to improve their performance regarding control accuracy and energy efficiency. This study analyses the strengths and weaknesses of simultaneous controls for the amount of air and its temperature by use of on-demand and predictive control strategies responding to two different outdoor conditions. The framework performs the comparative analyses of an on-demand model, which reacts immediately to indoor conditions, and a predictive model, which provides reference signals derived from data learned. Two models are combined to make a comparison of how much more efficient the combined model operates than each model when abnormal situations occur. As a result, when the two models are combined, its efficiency improves from 20.0% to 33.6% for indoor thermal dissatisfaction and from 13.0% to 44.5% for energy use, respectively. This result implies that in addition to creating new... [more]

In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of... [more]

Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be... [more]

Artificial Neural Networks (ANNs) have proven to be a powerful tool in many fields of knowledge. At the same time, evolutionary algorithms show a very efficient technique in optimization tasks. Historically, ANNs are used in the training process of supervising networks by decreasing the error between the output and the target. However, we propose another approach in order to improve these two techniques together. The ANN is trained with the points obtained during an optimization process by a genetic algorithm and a flower pollination algorithm. The performance of this ANN is used as a stop criterion for the optimization process. This new configuration aims to reduce the number of iterations executed by the genetic optimizer when learning the cost function by an ANN. As a first step, this approach is tested with eight benchmark functions. As a second step, the authors apply it to an air jet impingement design process, optimizing the surface temperature and the fan efficiency. Finally, a... [more]

The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. More... [more]

This paper focuses on the influence of detailed engineering maturities on offshore engineering, procurement, and construction (EPC) project procurement and construction cost performance. The authors propose a detailed engineering completion rating index system (DECRIS) to estimate the engineering maturities, from contract award to beginning of construction or steel cutting. The DECRIS is supplemented in this study with an artificial neural network methodology (ANN) to forecast procurement and construction cost performances. The study shows that R2 and mean error values using ANN functions are 20.2% higher and 19.7% lower, respectively, than cost performance estimations using linear regressions. The DECRIS cutoff score at each gate and DECRIS forecasting performance of total cost impact were validated through the results of fifteen historical offshore EPC South Korean mega-projects, which contain over 300 procurement cost performance data points in total. Finally, based on the DECRIS an... [more]

Wind power has grown popular in past recent years due to environmental issues and the search for alternative energy sources. Thus, the viability for wind power generation projects must be studied in order to attend to the environmental concerns and still be attractive and profitable. Therefore, this article aims to perform a sensitive analysis in order to identify the variables that influence most in the viability of a wind power investment for small size companies in the Brazilian northeast. For this, a stochastic analysis of viability through Monte Carlo Simulation (MCS) will be made and afterwards, Artificial Neural Networks (ANN) models will be applied for the most relevant variables identification. Through the sensitivity, it appears that the most relevant factors in the analysis are the speed of wind, energy tariff and the investment amount. Thus, the viability of the investment is straightly tied to the region where the wind turbine is installed, and the government incentives ma... [more]

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric.... [more]

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-f... [more]