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Mid-term load forecasting (MTLF) becomes an essential tool for today power systems, mainly in those countries whose power systems operate in a deregulated environment. Among different kinds of MTLF, this paper focuses on the prediction of daily peak load for one month ahead. This kind of load forecast has many applications like maintenance scheduling, mid-term hydro thermal coordination, adequacy assessment, management of limited energy units, negotiation of forward contracts, and development of cost efficient fuel purchasing strategies. However, daily peak load is a nonlinear, volatile, and nonstationary signal. Besides, lack of sufficient data usually further complicates this problem. The paper proposes a new methodology to solve it, composed of an efficient data model, preforecast mechanism and combination of neural network and evolutionary algorithm as the hybrid forecast technique. The proposed methodology is examined on the EUropean Network on Intelligent TEchnologies (EUNITE) test data and Iran’s power system. We will also compare our strategy with the other MTLF methods revealing its capability to solve this load forecast problem. [Copyright &y& Elsevier]

• A new method based on the discrete energy separation algorithm presents to HIF and LIF detection in distribution system. • Simple implementation and low computational burden are main advantages of the method. • The method detects HIF and LIF from other disturbances with high detection accuracy and low detection time even in noisy condition. • The method has proper performance in detection of recorded real HIFs in distribution system. • CT saturation has no impact on the detection accuracy and time of the method in LIF detection. Due to the low fault current of high impedance fault (HIF), it is not recognized by overcurrent relays. Moreover, due to the similar characteristics of HIF and other disturbances like capacitor bank (CB) and non-linear load (NL) switching, detection of HIF is one of the main challenges in protection of distribution system. Furthermore, detection of low impedance fault (LIF) is important for protection system. In this paper, a new statistical method is proposed based on a discrete time separation algorithm of energy for detection of HIF and LIF from other disturbances to improve detection accuracy and time. The method consists of two main parts, the first one detects switching on/off the linear load, switching off the CB and switching off the NL from abnormal conditions, while the second part identifies the HIF and LIF from switching on the CB and switching on the NL. High detection speed, simple implementation, and low computational burden are advantages of the method. The method is tested on IEEE 13-node and IEEE 34-node distribution test systems, and a real feeder in Iran. Obtained results show that the high efficiency of the proposed method in detection of disturbances even in noisy conditions, with low detection time. [ABSTRACT FROM AUTHOR]

In this paper, an optimization model is developed to determine the best size of a stand-alone hybrid renewable energy system (HRES) for electrification to a remote area located in Kerman, Iran. The model is defined based on three decision variables related to the system components, namely, total area occupied by the set of PV panels (a continuous variable), total swept area by the rotating turbines’ blades (a continuous variable) and the number of batteries (an integer variable). In order to find the optimal values of the variables, particle swarm optimization (PSO) and some of its variants are proposed. Due to the non-linearity and non-convexity of the sizing problem, PSO which is an efficient population-based heuristic technique can be a good candidate. Particles of PSO probe the search space to minimize the life cycle cost (LCC), ensuring at the same time certain level of system reliability. Simulation results reveal that the PV/WT/battery system is the most cost-effective one and adaptive inertia weight-based PSO algorithm yields more promising results than the other PSO variants. [ABSTRACT FROM AUTHOR]

Abstract: Various studies have been conducted thus far to classify the seismicity regions of Iran into zones that share similar seismic characteristics. In these studies, seismotectonic models have been presented that implement various information layers based mostly on human expert judgment. The purpose of the present research is to develop a seismotectonic model using all of the available information about the layers along with an automated smart clustering algorithm. In this paper, based on a review of past research studies, a combined model is presented, which involves the combination of the four most-referenced seismotectonic models. To build upon past studies, a smart algorithm called the self organizing map (SOM) was selected for clustering the seismotectonic data of provinces. Input layers, such as earthquake coordinates, seismicity, faults distribution, topography, gravity, and magnetism, were used to execute the SOM smart algorithm. Based upon previous studies, the number of clusters was selected in the range of 5–25. Three of the SOM algorithm outputs, which were more compatible with the combined model, were selected for use in this study. According to the clustering validation methods and expert judgments, the highest compatibility belongs to the models that were produced either by all of the information or by the combination of seismicity, faults, and topography layers. Implementation of the clustering validation methods indicated that the optimum number of seismotectonic zones is between 9 and 13. This study emphasizes that the application of smart methods along with various geological, structural, subsurface, and aerial information layers will produce informative and reliable results. [Copyright &y& Elsevier]

The purpose of this paper is to present a new tool for design of integrated energy systems. The initial choice of the energy system components and the way they should interact is a crucial decision which the outcome of the design heavily relies on. Use of a physical law (instead of engineering judgment) as the basis of the decision making is the main advantage of the proposed approach over conventional approaches for design of community scale energy systems. The methodology has been implemented for design of a district heating system for an existing district in arid region of Iran. The optimum level of interaction between the energy system components has been identified by employing an optimization algorithm seeking to minimize the overall cost of the energy system. Some of the relative merits of the optimum design comparing to the present energy system are 17% increase of the exergy efficiency, 10.8% reduction in the amount of CO2 production per capita and 2% reduction in overall energy related costs. [Copyright &y& Elsevier]

Abstract: Considering the increasing role of microturbines in distributed generation of combined heat and power (CHP), the localization of microturbine design and manufacturing technology has received great importance in Iran. Providing a complete and comprehensive algorithm can be a major step in reaching the goal. In this paper, we present the cycle for the thermofluids design of a 100 KW microturbine which is carried out at the Niroo Research Institute of Iran to simultaneously generate both electricity and useful heat. The characteristics of this system are as follows: revolution speed of 47750rpm, single shaft, natural gas as fuel, single stage radial turbine/compressor and annular combustion chamber. Based on the thermodynamic characteristics of the cycle, the characteristics of different components of microturbine are determined throughout the study. According to the determined values, we expect the cycle to achieve an efficiency of approximately 13% without the recuperator and 25% with the recuperator. It must be noted that the designed microturbine is going through the final assembly stages at the time and the performance test has not been conducted yet. Therefore, the over-all characteristics curve of the system was not available at the time of preparing this article. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user''s experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (Pb) and the oil formation volume factor at bubble point (Bob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions. [Copyright &y& Elsevier]

Abstract: This paper deals with estimation of electricity demand of Iran based on economic indicators using Particle Swarm Optimization (PSO) Algorithm. The estimation is based on Gross Domestic Product (GDP), population, number of customers and average price electricity by developing two different estimation models: a linear model and a non-linear model. The proposed models are obtained based upon available actual data of 21years; since 1980–2000. Then the models obtained are used to estimate the electricity demand of the target years; for a period of time e.g. 2001–2006 and the results obtained are compared with the actual demand during this period. Furthermore, to validate the results obtained by PSO, genetic algorithm (GA) is applied to solve the problem. The results show that the PSO is a useful optimization tool for solving the problem using two developed models and can be used as an alternative solution to estimate the future electricity demand. [Copyright &y& Elsevier]

Abstract: In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates. [Copyright &y& Elsevier]

Abstract: In this paper, the stochastic optimal control algorithm (OPTCON) is used to calculate optimal fiscal and monetary policies under fixed, flexible and crawling peg exchange rate regimes for the third five year Iranian development plan (2000–2004). The results of the study show that in the absent of active monetary policy instruments, the government expenditures are greater and the optimal tax revenues are lower than the proposed values in Iran third development plan. However, under a flexible exchange rate regime, the optimal values of government expenditures are lower and the optimal values of tax revenues are greater than that proposed in Iran’s third development plan. The study also shows that using optimal macroeconomic policies lead to lower fluctuations in major macroeconomic variables. The main conclusion seems to be is that only under the flexible exchange rate regime, the macroeconomic goals of the plan can be achieved, the flexible exchange rate regime being recommended as a policy instrument for Iran’s fourth five year development plan. [Copyright &y& Elsevier]

Selection of non-deposit sites is a challenging issue affecting the application of supervised algorithms for modeling mineral exploration targets. For this, equal number of deposit and non-deposit sites has been widely applied for training purposes. In this paper, we investigated the effect of changes in the number of non-deposit sites on the effectiveness of exploration targeting models while the number of deposit sites is constant. The results obtained demonstrated that exploration targeting models are affected by the ratio of non-deposit and deposit sites. Thus, balancing between the number of deposit and non-deposit sites is an efficient way to produce more-reliable exploration targets when supervised algorithms are applied for modeling. The idea of this research came from the fact that mineralization is a rare event, and therefore, in a region of interest number of non-deposit sites is much more than that of deposit events. To illustrate the procedure proposed, we used an exploration dataset of porphyry Cu mineralization in Chahargonbad area, SE Iran. A sequence application of self-organizing map and multilayer perceptron neural network algorithm was applied to better illustration of the changing effects of the number of non-deposit sites on the ensuing exploration targeting models. • Counting of deposit and non-deposit sites is discussed for training purpose. • The number affects the ensuing exploration targeting models. • Making a balance between the two types of training data yields significant results. • Self-organizing map clustering approach leads to define non-deposit sites. [ABSTRACT FROM AUTHOR]

In this paper optimal and cost-effective design of a photovoltaic/wind turbine/fuel cell (PV/WT/FC), the renewable energy system is proposed with the objective of lifespan cost of hybrid system (LSCS) minimisation and considering reliability constraint as a maximum of load interruption probability (LIP) as LIPmax to supply the demand of residential-commercial centres (RCC) located in Iran based on real data of irradiance and wind speed of this region. A new hybrid meta-heuristic algorithm named hybrid grey wolf optimiser-sine cosine algorithm (HGWOSCA) based on the exponential decreasing function (EDF) with high accuracy and speed of optimisation in achieving to the global solution is applied to determine the optimal size of system components. The superiority of the proposed HGWOSCA is compared with SCA, GWO and particle swarm optimisation (PSO) methods in designing of different combinations of the hybrid system and under varying reliability constraint. The results showed that the PV/WT/FC combination is the best combination in view of LSCS and LIPmax for supplying the RCC as a cost-effective and reliable combination. The superiority of the HGWOSCA in designing different combinations of the hybrid system is proved with fewer LSCS and LIP than the other methods with more convergence speed and accuracy. The results clearly showed that the use of hydrogen storage as a reserve power has well compensated the fluctuations in renewable sources production for meeting the RCC load to achieve optimal reliability. The results cleared that the cost of per kW of RCC supply for LIPmax = 1% and LIPmax = 5% is 0.853 and 0.764 $. The results indicated that increasing the failure rate of the components increases the system cost and weakens the reliability. In addition, the results showed that increased FC efficiency has reduced the level of reserve hydrogen, reduced the LSCS, and improved reliability. [ABSTRACT FROM AUTHOR]

• A multivariate modeling of primary geochemical haloes in a gold ore deposit was performed. • A new approach was used to provide anomaly model based on the MAF and U-spatial statistic methods. • Provides new target area for exploration under cover in the study area. An accurate separation of anomalous areas (values) in primary geochemical halos with heterogeneous spatial variations requires an appropriate algorithm that considers the complex patterns and multivariate relationships of the input geochemical data. There are several problems in multivariate modeling of geochemical compositional data, because such data present spurious values of spatial correlation between the variables. To address this problem, this paper proposes an improved U-spatial statistic algorithm for 3D multivariate anomaly modeling of the geochemical dataset with compositional nature at the Sari Gunay epithermal gold deposit, NW Iran. To perform 3D multivariate modeling, isometric logarithmic ratio (ilr), as an effective method to transform compositional data from the simplex to real space, and minimum/maximum autocorrelation factor (MAF), as a multivariate geostatistical method, were used in an integrated analysis. In comparison to the other separation techniques, this integrated approach has an advantage to control the heterogeneous spatial variations in the input data. In this approach, we improved the U-spatial statistic method by using the variogram tool theory. By using this approach, we identified the primary geochemical haloes associated with Au-As-Sb-Hg front halo elements, Cu-Ag-Pb near-ore halo elements and Ni-Co tail halo elements at the Sari Gunay gold depsoit. The results were validated by using contrast coefficient and exploratory index tools. By examining the 3D multivariate primary anomalous geochemical halos, zones of possible hidden epithermal gold and porphyry copper–gold mineralizations were recognized at this deposit for future exploration. [ABSTRACT FROM AUTHOR]

In this paper, we study the spatial distribution and temporal evolution of the postseismic deformation of the 2010–2011 Rigan earthquake sequence which occurred at the southern termination of the East Lut fault system, southeast Iran. One-year GPS measurements after the Rigan earthquake sequence reveals right-lateral postseismic displacement along the East Chahqanbar fault and left-lateral postseismic displacement along the South Chahqanbar fault. To investigate the deformation variations in time and space, InSAR time-series of COSMO-SkyMed images is applied using the Small Baseline Subset algorithm. The results confirm a clear cumulative postseismic signal increasing to 8 mm during the first five months following the first mainshock in the direction of the coseismic displacement. The cumulative postseismic displacements are well correlated with the cumulative number of the aftershocks and their associated moment release. Considering this correlation and the observation of a sharp discontinuity along the coseismic fault in the displacement map, it is concluded that the after-slip mechanism is responsible for the observed postseismic deformation in the study region. This study is the first observation of a short-term postseismic motion in eastern Iran through geodetic data in contrast with long-lasting postseismic displacements following the earthquakes that occurred around Lut block. Modeling of the postseismic displacement results in a distributed slip pattern with a maximum slip of 0.8 m on the fault plane responsible for the 2010 Rigan coseismic deformation. This indicates that the postseismic deformation on barriers remained unbroken during the mainshock. • We investigate the postseismic deformation of the Rigan earthquake sequence using GPS data and InSAR COSMO-SkyMed images. • We estimate the cumulative signal increasing to 8 mm following the first mainshock. • We observe a maximum postseismic slip of 0.8 m on the same fault plane responsible for the earthquake. • We observe short-term and deep afterslip postseismic motion for the Rigan earthquake sequence. • We find out that the observed postseismic motion was dominantly seismic. [ABSTRACT FROM AUTHOR]

In this paper, optimal designing and energy management of hybrid photovoltaic/wind/fuel cell (PV/WT/FC) system is presented with cost of hybrid system life span (CHSLS) minimizing and considering loss of load interruption probability (LOLIP) for Recreational Center of Gonbad (RCOG), a remote area region in Iran country using actual irradiance and wind speed data of this region. A meta-heuristic improved sine–cosine algorithm (ISCA) is used based on nonlinearly decreasing inertia weight strategy (NDIWS) for improving global and local search of the conventional SCA to find the optimal combination of hybrid system. In designing problem the optimal size of hybrid system components is determined using ISCA considering CHLS and LOLIP to satisfy the RCOG load demand in most cost-effective way. The performance of the proposed ISCA is compared with conventional SCA and well-known PSO methods in different combinations and under LOLIP changing conditions. Simulation results showed that the ISCA finds easily the optimal combination as PV/WT/FC system with lower CHSLS and better LOLIP. Also WT/FC system is not economical due to high CHSLS for RCOG. The results demonstrated that the average cost of per kW RCOG load supply using the PV/WT/FC for maximum LOLIP of 1% is equal to 0.87$ and for maximum LOLIP of 5% is 0.76$. The results showed the contribution and management of PV, WT and fuel cell together as PV/WT/FC is made a reliable and cost-effective energy system to supply the remote area application like RCOG. Moreover superiority of the ISCA than the SCA and PSO is proved with better convergence speed and accuracy. Also, the effect of some effective factors on the system designing is evaluated. • Hybrid system designing for RCOG load supply using ISCA, case study for Iran. • Optimal sizing of PV/WT/FC with minimizing the CHSLS and respect to the LOLIP. • Determination of reliable and cost-effective system combination to supply RCOG load. • Superiority of the ISCA performance compared to SCA and well-known PSO method. • Investigating the effect of some economic and technical factors on system designing. [ABSTRACT FROM AUTHOR]

• A novel healthcare waste location-routing problem is developed with a new perspective in healthcare logistics networks. • A stochastic essence for the emission of contamination is considered. • The management of medical wastes in Coronavirus Disease 2019 is focused. • A real case study is modeled to manage the logistics of infectious and non-infectious healthcare wastes. • A Multi-Objective Water-Flow like Algorithm with novel operators is developed and compared with the others. This paper presents a novel healthcare waste location-routing problem by concentrating on a new perspective in healthcare logistics networks. In this problem, there are healthcare, treatment, and disposal centers. Locations of healthcare centers are known, however, it is required to select appropriate locations for treatment, recycling, and disposal centers. Healthcare wastes are divided into infectious and non-infectious wastes. Since a great volume of healthcare wastes are infectious, the emission of contamination can have obnoxious impacts on the environment. The proposed problem considers a stochastic essence for the emission of contamination which depends on the transferring times. In this respect, transferring times between healthcare and treatment centers have been considered as normal random variables. As transferring time increases, it is more likely for the contamination to spread. Having visited a treatment center, infectious wastes are sterilized and they will no longer be harmful to the environment. This research develops a bi-objective mixed-integer mathematical formulation to tackle this problem. The objectives of this model are minimization of total costs and emission of contamination, simultaneously. Complexity of the proposed problem led the researchers to another contribution. This study also develops a Multi-Objective Water-Flow like Algorithm (MOWFA), which is a meta -heuristic, to solve the problem. This algorithm uses a procedure based on the Analytical Hierarchy Process (AHP) to rank the non-dominated solutions in the archive set. By means of a developed mating operator, the MOWFA utilizes the best ranked solutions of the archive in order to obtain high quality offspring. Two neighborhood operators have been designed for the MOWFA as the local search methods. Extensive computational experiments have been conducted to evaluate the effectiveness of the MOWFA on several test problems compared with other meta -heuristics, namely the Multi-Objective Imperialist Competitive Algorithm (MOICA) and Multi-Objective Simulated Annealing (MOSA). These experiments also include a real healthcare waste logistic network in Iran. The computational experiments demonstrate that our proposed algorithm prevails these algorithms in terms of some well-known performance evaluation measures. [ABSTRACT FROM AUTHOR]