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Power consumption of wellbore drilling in oil and gas exploitations count for 40% of total costs, hence power saving of WBM (water-based mud) by adding different concentrations of Al2O3, TiO2 and SiO2 nanoparticles is investigated here. A high-speed Taylor–Couette system (TCS) was devised to operate at speeds 0–1600 RPM to simulate power consumption of wellbore drilling using nanofluids in laminar to turbulent flow conditions. The TCS control unit uses several sensors to record current, voltage and rotational speed and Arduino microprocessors to process outputs including rheological properties and power consumption. Total power consumption of the TCS was correlated with a second-order polynomial function of rotational speed for different nanofluids, and the correlated parameters were found using an optimization technique. For the first time, energy saving of three nanofluids at four low volume concentrations 0.05, 0.1, 0.5 and 1% is investigated in the TCS simulating wellbore drilling operation. It is interesting to observe that the lower concentration nanofluids (0.05%) have better power savings. In average, for the lower concentration nanofluids (0.05%), power was saved by 39%, 30% and 26% for TiO2, Al2O3 and SiO2 WBM nanofluids, respectively. TiO2 nanofluids have better power saving at lower concentrations of 0.05 and 0.1%, while Al2O3 nanofluids have saved more power at higher concentrations of 0.5 and 1.0% compared with their counterpart nanofluids.

In this study, the performance of a system consisting of an organic Rankine cycle (ORC) for generating power and an electrolyzer for producing hydrogen with a zeotropic mixture as working fluid to recover waste heat in a geothermal flash-binary cycle is investigated from energy and exergy point of view. The study also investigates the effect of using zeotropic mixtures with different compositions as the ORC's working fluid rather than pure fluids. Using the particle swarm optimization (PSO) algorithm, the optimization is performed to maximize the power production of the entire system. The results show that using the combination of pentane with other pure fluids as working fluid led to improved system performance in terms of power and hydrogen output. In view of ORC unit power output (31.51 kW), overall system power output (128.16 kW), and hydrogen output (0.39626 kg h−1 per kilogram of geothermal water), the pentane (0.54)/butene (0.46) mixture produces the best results. Under optimal operating conditions, the rate of exergy destruction, exergy and energy efficiency for the whole system is equal to 95.81 kW, 37.25% and 20.17%, respectively. It is also found that the composition of the zeotropic mixture has a significant impact on the performance of the ORC as well as hydrogen production, which is associated with the extent of temperature glide in the mixture. When pentane (0.54)/butene (0.46) is used as the working fluid, the highest improvement related to use of zeotropic mixture instead of pure fluid is observed, which increases the power output of the ORC unit and the hydrogen output of the system by, respectively, 22.53% and 23.02%. In the end, the effect of flash chamber pressure on the total power and hydrogen output is also investigated. This investigation shows that as this pressure increases, the total power output decreases. However, although a lower chamber pressure is more desirable, given the impact on the hydrogen production and the turbine size, it is preferable to keep the flash chamber pressure moderate.

Increasing energy demand has led to a substantial growth in the use of wind energy across the world, which can be attributed to the low initial and running costs and rapid and easy deployment of this technology. The development of hydrogen from wind energy is an excellent way to store the excess wind power produced, as the produced hydrogen can be used not only as clean fuel but also as input for various industries. Considering the good wind potentials of Yazd province, the variety of industries that are active in this area, and the central location of this province in Iran, which gives it ample access to major transport routes and other industrial hubs, hydrogen production from wind power in this province could benefit not only this region but the entire country. Given these considerations, we conducted a technical, economic, and environmental assessment of the potential for wind power generation and hydrogen production in Yazd province. Overall, the assessments showed that the best locations for harvesting wind energy in this province are Bahabad and Halvan stations. For these two stations, it is recommended to use EWT DW 52-900 turbine to take advantage of its higher nominal capacity to achieve higher electricity and hydrogen output and emission reduction. For Abarkoh and Kerit stations, which have a low wind energy potential, it is recommended to use small turbines such as Eovent EVA120 H-Darrieus. Also, economic and technical assessments showed that it is not economically justified to harvest wind energy in Ardakan station. The results of ranking the stations with the Step-wise Weight Assessment Ratio Analysis (SWARA) and Evaluation based on Distance from Average Solution (EDAS) techniques showed that Bahabad station was introduced as the best place to produce hydrogen from wind energy.

In the view of the latest status and the potential of developing wind energy in South Africa, the present study aims to perform technical–economic–environmental​ analysis on a wind turbine system with HOMER software using the 20-years average data of the wind speed obtained from NASA’s database, for providing the electricity to residential buildings. The results showed that the Port Elizabeth station, had the lowest levelized cost of electricity (LCOE) with the value of -0.363 $/kWh when using the EOLO wind turbine, and the Bloemfontein station had the highest LCOE with the value of 1.601 $/kWh when using the Turby wind turbine. The results from the step-wise assessment ratio analysis (SWARA) weighting method demonstrated the wind penetration, total production, and capital cost as the most important sub-indices with the weights of 0.106, 0.095, and 0.091, respectively. Using the additive ratio assessment (ARAS), weighted sum method (WSM), and weighted aggregated sum product assessment (WASPAS) techniques, the cities under study were ranked, and the cities of East London and Bloemfontein were identified as the most suitable and the most unsuitable stations for the use of household-scale wind turbines, respectively.

Due to the devastating ecological effects and constrained reserves of fossil fuels, renewable energies are now globally accepted as viable alternative sources of energy. Among renewable energy sources, wind energy has become globally popular, primarily because wind farms can be rapidly built and easily maintained at a relatively low cost. Wind-powered hydrogen production is an effective solution for storing the excess energy output of wind farms. The hydrogen produced in this way can be used not only in fuel cells but also in cooling, oil, gas, and petrochemical fields. As a country devastated by war and instability, Afghanistan has major energy generation challenges and a substantially large power supply deficiency. However, there are good wind energy potentials in several parts of this country. There are also several hydrogen-consuming fields in Afghanistan that can benefit from hydrogen production from wind energy. This paper endeavored to distinguish the appropriate areas in Afghanistan for harvesting wind energy for hydrogen production using multi-criteria decision-making techniques. Eleven criteria were utilized to prioritize 20 Afghan provinces with wind energy potential. The Step-wise Weight Assessment Ratio Analysis (SWARA) was utilized to weight the criteria and Evaluation based on Distance from Average Solution (EDAS) were utilized to prioritize the provinces. Then, ARAS, TOPSIS, and VIKOR methods were used to validate the resultants. For criteria weighting with SWARA, “wind speed”, “wind power density” and “area of windy regions” with weights of 0.1423, 0.1356, and 0.1221 were introduced as the most significant criteria for this ranking. In all the rankings, Herat, Farah, and Jowzjan were identified as the top three most suitable provinces for wind power generation. The power output and hydrogen output to be achieved in Herat province using a 900-kW turbine were estimated to 2558.4 MW per year and 41.4 tons per year, respectively.

In recent years, hydrogen has constituted a clean energy carrier that can be gained by the use of renewable electricity. The most preliminary stage in the process of renewable hydrogen generation is to find the best place for exploiting the most energy. Thus, this study seeks to optimize the process of location selection for the construction of a solar power station. This evaluation is performed on 12 cities of Isfahan in Iran. After ascertaining 11 criteria of key importance, Window Data Envelopment Analysis (WDEA) Method is used to prioritize the cities according to the data for a period of 11 years. Consequently, the most promising site is technically and economically scrutinized as to hydrogen production using solar electricity. Results pertaining to the first part of the study showed that the city of Natanz was efficient over the entire studied period. Considering 4 cases of different performance rates, annual electricity generation using solar panel model X21-345 and hydrogen production using an alkaline electrolyzer were estimated for the city. The estimations indicated that hydrogen production under the worst and the best cases would be 2.22 kg and 5.55 leading to energy efficiency of between 2.5% and 7.1%, respectively. Finally, economic assessment proved promising results in which Levelized Cost of Electricity (LCOE) would be between 0.5317 and 1.6272 $/kWh and Levelized Cost of Hydrogen (LCOH) would vary from 0.7911 to 1.6778 $/kg.

Generation of energy across the world is today reliant majorly on fossil fuels. The burning of these fuels is growing in line with the increase in the demand for energy globally. Consequently, climate change, air contamination, and energy security issues are rising as well. An efficient alternative to this grave hazard is the speedy substitution of fossil fuel-based carbon energy sources with the shift to clean sources of renewable energy that cause zero emissions. This needs to happen in conjunction with the continuing increase in the overall consumption of energy worldwide. Many resources of renewable energy are available. These include thermal, solar photovoltaic, biomass and wind, tidal energy, hydropower, and geothermal. Notably, tidal energy exhibits great potential with regard to its dependability, superior energy density, certainty, and durability. The energy mined from the tides on the basis of steady and anticipated vertical movements of the water, causing tidal currents, could be converted into kinetic energy to produce electricity. Tidal barrages could channel mechanical energy, while tidewater river turbines can seize the energy from tidal currents. This study discusses the present trends, ecological effects, and the prospects for technology related to tidal energy.

Purpose Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives. Design/methodology/approach In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software. Findings Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified. Originality/value As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.

This study seeks to scrutinize the economic aspects of establishing the proposed system for producing electricity and hydrogen in the nominated city. For this, levelized cost of wind-generated electricity, levelized cost of seawater desalinated using wind energy, levelized cost of wind-powered hydrogen, payback period of investing on electricity, and hydrogen production are predicted. The results indicated that levelized cost of wind-generated electricity would vary from 0.0208 to 0.053 US$/kWh under different cases and scenarios. This range regarding levelized cost of seawater desalinated using wind energy was between 0.0147 and 0.0404 US$/m3 and also the amount of levelized cost of wind-powered hydrogen was guessed to be from 7.0074 to 10.5667 US$/kg. All values of payback period calculated as to wind electricity were less than half of the project lifetime. In addition, payback period of generating hydrogen was arguable only for the turbine with a rated power of 900 kW.

Solar energy has been used to preserve agricultural products which require drying through a consistent application of relatively low heat. In the present study, the feasibility of implementation the solar dryer system in the city of Yazd in Iran is evaluated by examining the effective factors. By identifying factors which may somehow affect the design, construction and operation of solar drying systems, a questionnaire was prepared and distributed. Among the results of the analysis, six main factors were identified: “performance,” “geographical location,” “infrastructures,” “interactions,” “financial support” and “social, cultural and political issues.” According to the results obtained from factor analysis and with Structural Equations Modelling (SEM) method, a model was introduced. Subsequently, the final model was developed with the use of path analysis method and with AMOS. Based on the final model, the three factors of infrastructures (with the coefficient of 0.44), interactions and cultural, social and political issues (both with the coefficient of 0.12) directly affect the possibility of implementing the solar dryer in Yazd. According to the proposed model, it was concluded that authorities should mainly focus their attentions on finance, science, economy and infrastructures to develop and promote use of solar drying systems.

Purpose To design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters. Design/methodology/approach To improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution. Findings In this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method. Originality/value Changes in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

The capacity factor is an important wind turbine parameter which is ratio of average output electrical power to rated electrical power of the wind turbine. Another main factor, the AEP, the annual energy production, can be determined using wind characteristics and wind turbine performance. Lower rated power may lead to higher capacity factor but will reduce the AEP. Therefore, it is important to consider simultaneously both the capacity factor and the AEP in design or selecting a wind turbine. In this work, a new semi-empirical secondary capacity factor is introduced for determining a rated wind speed at which yearly energy and hydrogen production obtain a maximum value. This capacity factor is expressed as ratio of the AEP for wind turbine to yearly wind energy delivered by mean wind speed at the rotor swept area. The methodology is demonstrated using the empirical efficiency curve of Vestas-80 2 MW turbine and the Weibull probability density function. Simultaneous use of the primary and the secondary capacity factors are discussed for maximizing electrical energy and hence hydrogen production for different wind classes and economic feasibility are scrutinized in several wind stations in Kuwait.

Geothermal energy is a type of renewable energy with high availability and independence from climatic and atmospheric conditions. It has been shown that geothermal energy is technically, economically and environmentally more suitable for hydrogen production than other renewable sources. Hydrogen has wide applications in many fields including cooling, oil, gas, petrochemical, nuclear, and energy industries. Afghanistan has significant potential in geothermal power generation and also several hydrogen-consuming industries that provide opportunities for geothermal-based hydrogen production. This study attempted to find suitable locations for the construction of geothermal power plant for hydrogen production in Afghanistan. Given the multitude of criteria involved in the choice of location, evaluations and comparisons were performed using multi-criteria decision-making methods. Nine criteria were used to evaluate 17 Afghanistan provinces in terms of suitability for geothermal-based hydrogen production. The SWARA (Stepwise Weight Assessment Ratio Analysis) method was used to weight the criteria and then the ARAS (Additive Ratio Assessment) method was used to rank the provinces. The results were validated. The results showed that Sari pul, Balkh and Herat are the most suitable Afghanistan provinces and Zabul, Ghor and Kandahar are the least suitable Afghanistan provinces for geothermal-based hydrogen production. The three methods produced almost identical rankings with only minor differences in the overall ranking of some provinces.

PurposeRural areas are one of the effective regions in economy and self-sufficiency field especially in agricultural and livestock section. Planning in the rural section and the effort in solving the problems of farmers lead to increase their interest in farming and manufacturing in the villages and decrease their migration to the cities and metropolitans. Therefore, the present study aimed at feasibility of electricity to a rural household in Iran using off-grid solar-based hybrid system.Design/methodology/approachIn renewable energy projects, a successful evaluation requires suitable criteria so that one can properly analyze the operational behavior of all feasible scenarios. In the present paper, HOMER software has been used for this purpose for a village with no access to electricity grid (Bar Aftab-e Jalaleh, Iran). Due to drastic fluctuation of fossil fuel prices and varied solar radiations in various years because of climate change, sensitivity analysis has been performed using HOMER.FindingsIn the optimum status economically, 70% of needed energy is provided by solar cells at the price 0.792 $/kWh. The comparison between the optimum condition economically and the condition that only use fossil fuels revealed that the return on investment will occur after less than 2 years and have remained profitable over 23 years.Social implicationsThe authors hope that the results of this study can be used in planning of the authorities to realize the interests of people in this village.Originality/valueAccording to the surveys, despite Iran being the first country in terms of providing solar power to the villages, so far no socio-economic-environmental assessment has been done for a solar cell-based micro-grid in an off-grid mode for a remote village that is deprived of electricity from a national electricity grid. In addition, for the first time in Iran, the effect of the fuel price and solar radiation parameters variability on the performance of system have been investigated.

PurposeAcute shortage of potable water and energy supplies is expected to raise in developing countries in the near future. One solid way to address these issues is to exploit renewable energy resources efficiently. Hence, this study aims to investigate wind and solar energy use in the coastal areas of southern Iran for renewable-powered seawater desalination and hydrogen production systems.Design/methodology/approachTo accomplish the aforementioned purpose, five areas most prone to the problems in Iran, namely, Mahshahr, Jask and Chabahar ports and Kish and Hormoz islands were scrutinized. To ascertain the amount of wind and solar energy available in the areas, Weibull distribution function, Angstrom–Prescott equation and HOMER software were used.FindingsThe findings indicated that wind energy density in Kish was 2,014.86 (kWh/m2.yr) and solar energy density in Jask equaled to 2,255.7 (kWh/m2.yr) which possessed the best conditions among the areas under study. Moreover, three commercial wind turbines and three photovoltaic systems were examined for supplying energy needed by the water desalination and hydrogen production systems. The results showed that application of wind turbines with rated power of 660, 750 and 900 kWh in Kish could result in desalting 934,145, 1,263,339 and 2,000,450 (m3/yr) of seawater or producing 14,719, 20,896 and 31,521 (kg/yr) of hydrogen, respectively. Additionally, use of photovoltaic systems with efficiency of %14.4, %17.01 and %21.16 in Jask could desalinate 287, 444 and 464 (m3/yr) of seawater or generate 4.5, 7 and 7.3 (kg/yr) of hydrogen, respectively.Originality/valueCompared to the huge extent of water shortage and environmental pollution, there has not been conducted enough studies to obtain broader view regarding use of renewable energies to solve these issues in Iran. Therefore, this study tries to close this gap and to give other developing nations the idea of water desalination and hydrogen production via renewable energies.

Hydrogen has been a promising energy carrier to meet the world's energy needs as well as reduce pollutant emissions. Although many countries have policies and programs to expand hydrogen production, the potential for hydrogen production in different regions of Qatar has not yet been evaluated. Therefore, this paper, for the first time, evaluates the possibility of an average annual cogeneration of 14 kWh of electricity and 85 kg/day of hydrogen by a home-scale solar-wind system connected to the grid in Qatar. NASA's 20-year average of meteorological data, the electricity tariff and gasoline price in 2018, along with annual real interest rate, were used as inputs to HOMER software. The techno-econo-enviro analysis was done over a one-year period hour by hour. From the results, it was found that the lowest prices of hydrogen and electricity generated, with $ 2.092/kg and $ 11.495/kWh, were related to Grid and PV-Wind-Grid scenarios, respectively. Also, results indicated that Ar-Ruways station and PV-Wind-Grid scenario were the most environmentally suitable options that resulted in a CO2 emission rate of 1434 kg annually. To select just one station among five areas, a fuzzy method was deployed as a prioritization technique. Its results suggested that Doha Intl Airport site is the most suitable one for constructing solar-wind hybrid energy generation system.

Efficient utilization of wind energy depends on wind turbine selection and wind farm localization. The overall performance of a wind turbine is usually expressed by two important factors: the capac...

A technical–economic assessment was carried out in this study to determine the possibilities for wind and solar power generation in Afghanistan’s Helmand province. The results showed that most of the province has a solar irradiance of over 400 W/m2, and also showed that wind and solar power generated in the province can be up to twice as cheap as the official price of renewable power in Afghanistan. The most suitable site for solar and hydrogen production was found to be Laškar Gāh, where solar and hydrogen can be produced at a cost of 0.066 $/kWh and 2.1496 $/kg-H2, respectively. In terms of wind power production and hydrogen production from wind, the most suitable site was Sangīn, where wind power and hydrogen could be produced at costs of 0.057 $/kWh and 1.4527 $/kg-H2, respectively. Despite the high potential of wind and solar energy in the Helmand province, the most suitable place in this region to produce hydrogen from wind/solar energy was evaluated from technical, economic, and environmental perspectives with the Multi-Criteria Decision-Making (MCDM) method. The Stepwise Weight Assessment Ratio Analysis (SWARA) method was used for weighting criteria and the Weighted Aggregated Sum Product Assessment (WASPAS) method was used to prioritize locations. The results show that Sangīn is the most suitable place for the construction of a wind hydrogen power plant and Laškar Gāh is the most suitable place for the construction of a solar hydrogen power plant.

Energy requirements have increased dramatically due to industrialization, economic, and population growth. To meet this demand, and solve its challenges, such as climate change, renewable energies do play an important role. This research work aims at selecting the best renewable energy projects using a hybrid decision-making framework from environmental, economic, technical, and social aspects at a sub-national level. In this regard, a new hybrid fuzzy multi-criteria decision-making model is deployed in which Vise Kriterijumska Optimizacija I Kompromisno Resenje, distance from average solution, and additive ratio assessment methods are used. In addition, for the weighing of criteria, Fuzzy Shannon’s entropy is used. Furthermore, the North Khorasan province is nominated as a sub-national study area. The results show that among 30 sub-criteria, social acceptance, net-presented cost, and noise were the top three with weights of 0.1105, 0.1003, and 0.0988, respectively. Solar energy projects also accomplished high ranks with an overall score of roughly 0.22. After that, small hydropower got second place with a score of 0.187. Moreover, the ranking of cities indicates that Jajarm was the most suitable location for implementing renewable energy development with a score of 0.14. Finally, sensitivity analysis was carried out to show that the mathematical model possessed good robustness.

Purpose Wind as a major source of renewable energy has received tremendous attentions due to its unique features to reduce carbon emission and also to keep the environment safe. Nevertheless, to use wind energy properly, the environmental circumstances and geographical location related to wind intensity should be considered as a priority. Different factors may affect the selection of a suitable location for developments of wind power plants; thus, these factors should be considered concurrently to identify the optimum location of wind plants. Design/methodology/approach In this study, first, basic data envelopment analysis (DEA) was used, then dual DEA was used and, finally, Anderson Petersen (AP) model of dual DEA was selected to prioritize cities or decision-making units (DMUs). Numerical Taxonomy (NT) method was also used to assess the validity of AP dual model in DEA. The prescribed approach was applied for five cities in East Azerbaijan province of Iran. Findings The results indicate that wind power as a renewable energy can be harnessed in few cities, and the ranking by DEA illustrated that the city of Tabriz is the first priority. Practical implications Low environmental degradation effects in comparison to other methods and the ability to utilization at a widespread level include the benefits of using wind energy in the generation of electricity. In this regard, the study of relevant potentials and finding suitable locations for the deployment of wind energy utilization equipment are essential. Using DEA method helps us to choose optimal locations according to different criteria. Social implications Wind energy is justifiable in reducing social costs in comparison with fossil fuel plants, which includes negative effects, and its electricity can be used as a sustainable energy in the country's economic, social and cultural development. Originality/value For identifying the most proper location for development of wind power plants in Iran, DEA is applied for the first time to prioritize the suitable locations for installations of wind turbines among five different cities in the East Azerbaijan region. A number of crucial factors including land price, distance to power, rate of natural hazards, wind speed and topography are considered for location optimization of wind turbines for the first time. Also, to validate the results of DEA method, NT method is used to assess the validity of AP dual model in DEA.

In this study, feasibility of a new wind power generation system for urban application in Hormozgan Province of Iran is investigated. The wind turbine system in this study is a novel, aesthetically pleasing, noiseless, pollution-free, potentially cost-effective, and high efficiency design called tree-shaped wind turbine (TSWT). Techno-economic evaluation is performed on eight urban areas in the province using the software HOMER. Multi-criteria decision making approaches are used to prioritize the areas in terms of the best location for installing such a new system. The results of techno-economic analysis examining a wind power system consisting of 25 TSWTs show that the most electricity production would occur for Jask city which is 529,450 kWh/yr. Also, the least amount of electricity which is 339,275 kWh/yr belongs to Bandar Abbas. Considering the most important criteria including electricity production, levelized cost of electricity, population, land price, environmental impact, and frequency of natural disasters, data envelopment analysis, and the fuzzy technique for order of preference by similarity to ideal solution are employed to rank the cities. The results are validated by two different methods. Finally, it is suggested that Sirik is the best location for using the aforementioned wind turbine.

Purpose The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria. Design/methodology/approach The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months. Findings The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480. Originality/value Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

The rapidly growing of population in the developing countries and their lack of access to electricity, especially in the remote or rural areas, is causing huge challenges for on energy production. Energy is an enabler and a reliable energy supply is critical to sustainable socio-economic development for any nation. Most of Chad's people live in villages with no particular power supply system. Exploiting renewable energies is the only means of fostering development and improving people's welfare. This paper attempts at proposing an energy profile and storage model for Chad in vast remote towns. The paper addresses the key energy gap that is hindering on the development of such systems, it models and assess the potential on electricity generation and using hydrogen as surplus power storage system. A techno-econo-environmental survey on a solar-wind hybrid system in 25 towns in Chad is undertaken using NASA data and HOMER Software. Several hybrid scenarios of energy production and storage is analyzed. The results showed that in the electricity generation scenario, the average total NPC for the studied stations was $ 48164 and the average LCOE was $0.573. The lowest LCOE was related to Aouzou station with 0.507 $/kWh and the highest LCOE was obtained for Bol station with 0.604 $/kWh. In the simultaneous electricity and hydrogen generation scenario, the cheapest hydrogen ($4.695/kg) was produced in the “Grid” scenario, which was the same for all of the stations, with a total NPC of $2413770. The most expensive hydrogen ($4.707/kg) was generated in the “Grid-Wind” scenario and Bol stations with a total NPC of $2420186. The paper develops cost effective models for all hybrid systems combination for both electricity and hydrogen generation across Chad. These findings could help policy makers, investors and other developmental agencies make informed choices on energy access for sustainable development for rural communities in Sub Saharan Africa.

This study seeks to provide a new method by proposing three hybrid algorithms. The proposed algorithms include genetic neural network hybrid algorithm, simulated annealing neural network hybrid algorithm, and shuffled frog-leaping neural network hybrid algorithm. The efficiency and reliability of the presented hybrid algorithms in prediction of wind speed behavior were evaluated using meteorological data of the city of Abadeh for a 10-year period from 2005 to 2015. The forecasting horizon is monthly for this study. The study parameters consisted of TMAX, TMIN, VP, RHMIN, RHMAX, WIND SPEED, PRECIPITATION, and SUNSHINE HOURS. These eight parameters are used as the inputs, and one parameter (ET) is used as the output. Research findings show that the shuffled frog-leaping neural network hybrid algorithm providing a root mean square error value of 0.0761 and reliability of 0.91 is more suitable than other hybrid algorithms for prediction of wind speed behavior in the study area.

Purpose This paper aims to investigate the techno-economic feasibility of wind power potential for a tribal region located in Gachsaran in the South-West of Iran. Design/methodology/approach Techno-economic feasibility study and analysis of data were conducted by HOMER v2.68 software. Simulations and calculations were performed for 10 kW turbines, 8 Trojan L16P batteries, 12 kW converter and 12 kW generator. To anticipate the pay back period (PBP) or the time required to reach profitability, an engineering economic method named net equivalent uniform annual was applied. Findings The power plant construction cost, including the initial cost, installing, replacing and project operating costs for useful life of 20 years was equal to $40970. The net income of the project for each year was $8538 and the calculations were carried out using interest rate of 18%. Results indicated that PBP was 13 years which is lower than 20 years useful life of the turbine. Therefore, it is economically feasible to use this type of turbine for the nominated region. Originality/value There has not been conducted a research regarding remote areas in Iran; therefore, this study aims at closing this research gap. Moreover, this method could be used for any remote areas in any other developing country.

Due to acute problems caused by fossil fuels that threaten the environment, conducting research on other types of energy carriers that are clean and renewable is of great importance. Since in the past few years hydrogen has been introduced as the future fuel, the aim of this study is to evaluate wind and solar energy potentials in prone areas of Iran by the Weibull distribution function (WDF) and the Angstrom-Prescott (AP) equation for hydrogen production. To this end, the meteorological data of solar radiation and wind speed recorded at 10 m height in the time interval of 3 h in a five-year period have been used. The findings indicate that Manjil and Zahedan with yearly wind and solar energy densities of 6004 (kWh/m2) and 2247 (kWh/m2), respectively, have the greatest amount of energy among the other cities. After examining three different types of commercial wind turbines and photovoltaic (PV) systems, it becomes clear that by utilizing one set of Gamesa G47 turbine, 91 kg/d of hydrogen, which provides energy for 91 car/week, can be produced in Manjil and will save about 1347 L of gasoline in the week. Besides, by installing one thousand sets of X21-345 PV systems in Zahedan, 20 kg/d of hydrogen, enough for 20 cars per week, can be generated and 296 L of gasoline can be saved. Finally, the RETScreen software is used to calculate the annual CO2 emission reduction after replacing gasoline with the produced hydrogen.

The objective of this study was to perform a sensitivity analysis on the factors affecting wind site localization. A case study is selected based on 13 cities in the province of Fars in Iran which is equally applicable in any other wind sites. Then, the cities are ranked using the dual form of the data envelopment analysis model. Next, six criteria are adopted including wind conditions, population, Available Land condition, distance to distribution networks, rate of natural disasters, and the cost of land. In the sensitivity analysis of each criterion, first, the criterion under analysis is omitted from the model, and then the dual model is applied again to obtain a new ranking. Evaluating the results of the ranking for 13 studied cities indicate that the city of Shiraz is just sensitive to the population criterion and fell 11 places in the ranking by omitting this criterion. But the city is insensitive to any other criteria.

Purpose In hot and dry climates, air conditioning accounts for a large portion of total energy consumption; therefore, this paper aims to investigate the impact of sol-air temperature and ground temperature on the loss of cooling energy in hot and dry regions of Iran. Design/methodology/approach In line with this objective, the values of sol-air temperature along different directions and ground temperature at different depths were assessed with respect to climatic data of Yazd City. The impact of sol-air temperature and ground temperature on the rate of heat loss was investigated. So, energy loss of the walls aligned to four primary directions was calculated. This process was repeated for a 36 m2 building with three different shape factors. All analyses were conducted for the period from May to September, during which buildings need to be cooled by air conditioners. Findings Numerical analyses conducted for hot and dry climate show that sol-air temperature leads to a 41-17 per cent increase in the wall’s energy loss compared with ambient temperature. Meanwhile, building the wall below the surface leads to a significant reduction in energy loss. For example, building the wall 400 cm below the surface leads to about 74.8-79.2 per cent energy saving compared with above ground design. The results also show that increasing the direct contact between soil and building envelope decreases the energy loss, so energy loss of a building that is built 400 cm below the surface is 53.7-55.3 per cent lower than that of a building built above the surface. Originality/value The impact of sol-air temperature and ground temperature on the cooling energy loss of a building in hot and dry climate was investigated. Numerical analysis shows that solar radiation increases heat loss from building envelope. Soil temperature fluctuations decrease with depth. Heat loss from building envelope in an underground building is lower than that from building envelope in a building built above the ground. Three different shape factors showed that sol-air temperature has the maximum impact on square-shaped plan and minimal impact on buildings with east-west orientation.

There has been a great concern about shortage of potable water in many countries as well as Iran, because of the dramatic low rainfall during past few decades almost in all over the Iran. There is a great concern for implementing renewable desalination systems as it seems to be the only clean and environmental friendly source to the traditional fossil fuel powered systems. This study sought to simultaneously assess the reliability of electricity and cost in off grid photovoltaic systems for a photovoltaic reverse osmosis desalination system in 9 districts of Bushehr Province in Iran. Solar data used in this study contains indices of “Clearness Index” and “Daily Solar Radiation” over a period of 16 years from 2000 to 2016. HOMER and Excel were used for technical-economic feasibility of the proposed systems. For this study, a new model of BWRO-2S-130/75 desalination system is tested and proposed. The results indicate that the proposed photovoltaic systems are technically and economically feasible. It was also found that the reliability of off grid photovoltaic system using fuzzy time function provided better results than using the simple method. The results of the evaluation of proposed photovoltaic systems by HOMER software shows that annual electricity production for Delvar and Deylam port are 72,336 and 47,915 kWh respectively which is promising. Also, maximum of 228 m3 and a minimum of 148 m3/day of potable water can be produced with cost of 1.96 to 3.02 $/m2 for Delvar and Deylam port respectively. Results indicate that using the proposed system of desalination would be economically feasible for Delvar, since the predicted water cost is cheaper compared to existing water price of $2.5. The new model of BWRO-2S-130/75 desalination can meet the water demand of the selected city.

Wind power potential by itself is not a good indicator of the suitability of a region for wind power generation for different purposes. Economic attractiveness is a better indicator in this regard as it stimulates the involvement of private businesses in this sector. Naturally, the shorter is the payback period or the time required to reach profitability, the more attractive will be the project. Considering the high wind energy potential of some regions of Iran, this study evaluates the wind energy available for generating electricity as well as hydrogen by industrial and agricultural sectors in four cities of Ardebil province, namely Ardebil, Khalkhal, Namin, and Meshkinshahr, and then conducts an econometric analysis accordingly. Wind power potentials are evaluated using the energy pattern factor and Weibull distribution function based on 5-year meteorological data of the studied regions. Economic evaluations are performed based on the present worth of incomes and costs, which are estimated for two models of wind turbines with 3.5 and 100 KW rated power. Results indicate that the cities of Namin and Ardebil with wind power densities of respectively 261.68 and 258.99 W/m2 have the best condition. The economic analysis conducted for turbines shows that for Ardebil, installation of the 3.5 KW and 100 KW turbines will have a payback period of 13 and 5 years, respectively. For Khalkhal, Namin, and Meshkinshahr, the only feasible option is installation of the 100 KW turbine, which would result in a payback period of respectively 10.2, 6.1 and 8.7 years. Then it is investigated how much hydrogen can be gained if these private sectors invest in producing hydrogen using nominated wind turbines.

With the advent of new technologies and globalization of business, supply chains have turned into indispensable tools for gaining competitive advantage. The application of new technologies like blockchain can benefit sustainable energy supply chains by improving chain and logistics operations in the areas of trust, transparency and accountability, cooperation, information sharing, financial exchanges, and supply chain integration. However, the efforts to adopt such technologies in supply chains tend to face many challenges and challenges, which can seriously threaten their success. Therefore, it is crucial to carefully examine the challenges to blockchain technology application. This research focuses on identifying the criteria and challenges to the application of blockchain in renewable energy supply chains and also ranks the identified challenges in terms of their capacity to disrupt the process. The applicability of the suggested structure is examined in a case study of the renewable energy supply chain of Iran. In this study, the challenges are evaluated and ranked by the hybrid developed methods by the integration of the concept of gray numbers into the gray stepwise weight assessment ratio analysis (SWARA-Gray) and the gray evaluation based on distance from average solution (EDAS-Gray). Another group of hybrid methods including the gray weighted sum method (WSM-Gray), the gray complex proportional assessment (COPRAS-Gray), and the gray technique for order of preference by similarity to ideal solution (TOPSIS-Gray) is used to validate the results. The rankings obtained from all of these techniques show high degree of correlation. Among the identified challenges, "high investment cost" is found to be the most important challenge to the application of blockchain in sustainable energy supply chains.

The emerging environmental consequences of overdependence on fossil fuels have pushed many countries to invest in clean and renewable sources of power. Countries like Iran where these sources can be found in abundance can take advantage of this potential to reduce their dependence on fossil fuels. This study investigated the feasibility of the standalone use of a hybrid renewable energy system (HRES) to power buildings in the Bostegan village in the Hormozgan province of Iran. Technical, economic, and environmental assessments were performed with the help of the Hybrid Optimization of Multiple Energy Resources (HOMER) software, and the optimal configuration for the system components was determined accordingly. The results showed that the simultaneous use of wind and solar systems with a converter and a backup system comprised of a diesel generator and batteries will be the most economic option, offering electricity at a cost of 1.058 USD/kWh and with a renewable fraction of 64%. After selecting the most optimal system using the step-wise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assessment (WASPAS) techniques, a sensitivity analysis with 27 parameter settings was performed to determine the effect of fuel price fluctuations and the uncertainty in the renewable energy potentials on the results. This analysis showed that in the worst-case scenario, the price of electricity will reach as high as 1.343 $/kWh. In the end, the study investigated an alternative scenario where the generated power is used for hydrogen production, which showed that the system output can be used to produce 643.63 ton-H2/year.

With the rising demand for food products and the direct impact of climate change on food production in many parts of the world, recent years have seen growing interest in the subject of food security and the role of rainfed farming in this area. Machine learning methods can be used to predict crop yield based on a combination of remote sensing data and data collected by ground weather stations. This paper argues that forecasting drylands farming yield can be reliable for management purpose under uncertain conditions using machine learning methods and remote sensing data and determines which indicators are most important in predicting the yield of chickpea. In this study, the yield of rainfed chickpea farms in 11 top chickpea producing counties in Kermanshah province, Iran, was predicted using three machine learning methods, namely support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN). To improve prediction accuracy, for each county, remote sensing data were overlaid by the satellite images of rainfed farms with a suitable slope and altitude for rainfed farming. An integrated database was created by combining weather data, remote sensing data, and chickpea yield statistics. The methods were evaluated using the leave-one-out cross-validation (LOOCV) technique and compared in terms of multiple measures. Given the sensitivity of rainfed chickpea yield to the time of data, the predictions were made in two scenarios: (1) using the averages of the data of all growing months, and (2) using the data of a combination of months. The results showed that RF provides more accurate yield predictions than other methods. The predictions of this method were 7–8% different from the statistics reported by the Statistical Center and the Ministry of Agriculture of Iran. It was found that for pre-harvest prediction of rainfed chickpea yield, using the data of the March–April period (the averages of two months) offers the best result in terms of the correlation coefficient for the relationship between the yield and the predictor indices.

Despite the fundamental role of the social aspect in the implementation of sustainability in the bio-based industries, most of the sustainability assessments research have addressed the environmental and economic dimensions. However, the social dimension has been neglected and it can cause an irreparable outcome in the biotechnology industries. Following this issue, this study propounds a modified systemic approach for a social sustainability impact assessment of the treatment technologies for converting waste into bioenergy, based on a review on the common social assessment methods. As it is known, the guideline presented by the United Nations Environment Program (UNEP) and the Society of Environmental Toxicology and Chemistry (2009) due to considering social life cycle assessment has a comprehensive look at the stakeholders. Therefore, in this paper, UNEP method was selected. However, it needs to be modified based on the bio-energy supply chain derived from municipal solid waste. For this purpose, the bioenergy value chain derived from municipal solid waste was designed and combined with UNEP guideline, to complete the level of stakeholder subgroups and the levels of the indicators. The final method of the social assessment system was presented to the board of experts and finalized. In order to design the measurement part of the social assessment system, because of a multi criteria decision making nature of the social sustainability evaluation of the conversion technologies of municipal solid waste to bio-energies, a recent developed multi-criteria decision making method so-called Best Worst Method (BWM) was used in two stages. The criteria are ranked according to their average weight obtained through Best Worst method. One of the major novelties in this research is the way of application of the best worst technique in the second stage. The model was implemented in the case of Tehran as one of the pioneering Iranian municipalities with high potential to produce bioenergy. The results of this study help decision makers to decide where to concentrate their attention during the implementation stage, and to increase social sustainability in their bioenergy supply chains derived waste.

Roughly 8 percent of the final energy used for heating and cooling of buildings and industries around the world is provided by renewable energies. Nevertheless, a major part of the energy consumed for heating is based on fossil fuels. Turkey's energy policies are aimed at providing secure, sustainable, and cost-effective energy, enhancing domestic energy production, and increasing energy efficiency in order to reduce consumption. To this end, renewable energies play an important role in Turkey's energy policies and, given the high energy potentials in Turkey, if these potentials are sufficiently realized, they can make a significant contribution to meeting future’s energy demands. Therefore, in the present work for the first time, the use of solar water heaters (SWHs) in household scale has been investigated, by using climate data of radiation intensity and temperature for 45 stations in Turkey. Technical and environmental analysis was carried out by commercial software TSOL PRO 5.5 on two types of flat plate (FP) water heaters and evacuated tubes (ET), also, these stations are ranked using GAMS 24.1 software and DEA-BCC and DEA-Additive methods. The results show that the efficiency of ET water heaters is better than that of FP water heaters at all stations. For the FP SWH, the annual generation of heating, the annual heating generation for domestic hot water, and annual CO2 emission mitigation were 132,605 kWh, 120,365 kWh, and 68.4 t, whereas the same parameters were 228,814 kWh, 128,578 kWh, and 93.4 t for the ET SWH. For the FP collector, the results show that Akhisar, Bodrum, Finike, Hakkari and Iskenderun stations are superior stations and Sinop is the inappropriate station in both models, DEA-BCC and DEA-Additive models. On the other hand, for ET collector, the results show that Akhisar, Bodrum, Finike, Hakkari, Dalaman and Iskenderun stations are superior stations in both DEA-BCC and DEA-Additive methods and in DEA-BCC method, Zonguldak station and in DEA-Additive method Sinop station is the inappropriate station.

In recent decades, many countries have shown a growing interest in the use of renewable energies for power generation. Geothermal energy is a clean and environmentally friendly source of renewable energy that can be used to produce electricity and heat for industrial and domestic applications. While Afghanistan has undeniably good geothermal potential that can be utilised to alleviate the country&rsquo, s current energy limitations, so far this potential has remained completely untapped. In this study, the suitability of 21 provinces for geothermal project implementation in Afghanistan was evaluated using multiple multi-criteria decision-making (MCDM) methods. The stepwise weight assessment ratio analysis (SWARA) method was used to weigh each criterion while the additive ratio assessment (ARAS) method was used to rank potential geothermal sites. The technique for order of preference by similarity to ideal solution (TOPSIS), the vlse kriterijumsk optimizacija kompromisno resenje (VIKOR), and the weighted aggregated sum product assessment (WASPAS) methods were also used in this study. These rankings were then examined via sensitivity analysis which indicated that a 5% change in criteria weights altered the rankings in all methods except the VIKOR method. Volcanic dome density was ranked the most important criteria. All the methods identified Ghazni province as the most suitable location for geothermal project implementation in Afghanistan.

Energy crisis concentrates attentions in the field of building energy consumption through optimization of HVAC control systems. Studying the HVAC systems and optimizing them will help to save energy. Exergyis defined as a new energy function that can maximize accessible work by the second law of thermodynamics. The present study, discusses about HVAC system that is in operation for mushroom growing hall. The Exergy destruction is calculated for HVAC and the whole system and is linked to effective parameters as independent variables. Adaptive neuro fuzzy inference system (ANFIS) and multi layered perceptron (MLP) methods are used to model the studied system. Accordingly, after training by different number of neurons in the hidden layer for MLP network and by different types of membership function for ANFIS method, 10 numbers of neurons were selected as the best number of neurons for MLP network and Gaussian type of membership function for ANFIS method. The results indicate that MLP by consumption of 11.556 kj/s more energy compared to ANFIS, imposes 1.343 × 10−5 $/s more cost and 2.687 × 10−4 m3/s more consumption of natural gas. Therefore, applying ANFIS model prevents energy, time, cost losses and more GHG emission, so it can be the best and suitable model to adopt in real system.

Farmers across the world have long used chemical fertilizers to achieve higher production outputs. One of the basic inputs for the production of nitrogen fertilizer is fossil fuel, which is not only finite but also has tangible impacts on the environment. This study aims to determine the most suitable renewable energy resource (RER) for the production of fertilizer in Iranian farmlands. The resources considered in this study were photovoltaic energy, biomass energy, wind energy, and solar thermal energy. This assessment was carried out in terms of six general criteria derived from a PESTEL (Political, Economic, Socio-cultural, Technological, Environmental and Legal) analysis, and thus 20 sub-criteria were obtained with the help of experts. The criteria were weighted using the Fuzzy AHP (Analytic Hierarchy Process) method. Because of the use of criteria with crisp, fuzzy, and interval-type values, the ranking was performed using the extended TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method. A sensitivity analysis was performed to determine the effects of the sub-criteria on the results. The results showed that the technological criterion is the most important measure for this assessment, and that photovoltaic energy and wind energy are the top two options for powering chemical fertilizer production in Iranian farmlands.

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It is generally anticipated that larger wind turbines result in better electrical power generation in more efficient and economical manner. However, the question remains is that how large a wind turbine is the optimum size for a selected wind site? To answer this, three important objective functions, the levelized cost of electricity (LCOE), the capacity factor (CF) and a normalized annual energy production (AEP) are optimized versus the rated wind speed. Four empirical power curve models for large wind turbines and Weibull wind distribution are investigated to produce a Pareto front within range of practical wind conditions. It is observed that the optimized values of the objective functions are merely dependant on the Weibull shape factor, k. Also, the optimum rated wind speed is a linear function of the Weibull scale factor, c. Therefore, it is concluded that, there are limits for the optimum rated wind power and the costs. Consequently within the Pareto front, the lower rated power wind turbines may be selected to reduce LCOE albeit the higher rated power wind turbines may be adopted to produce higher AEP and CF.

The integration of renewable energy sources like wind and solar is very important to combat climate change, also to reduce carbon dioxide in many countries. Afghanistan with low energy consumption has a great potential for using renewable energies., also therefore, this study attempts to find suitable locations for constructing solar-wind power-plants using solar and wind data from 46 stations by HOMER and GIS softwares. Based on the available data, results indicate that Uruzgan and Balk stations, each with $12006 and $13941 respectively, have the lowest and highest net present cost. Also, in terms of using renewable energies (%) and producing CO2 emissions, Qandahar with 99% and 7 kg/yr and Balkh with 87% and 235 kg/yr are respectively the best and the worst stations from an environmental perspective. According to the results, average price per kWh of energy generated in Afghanistan is $0.638, average percentage of using renewables is about 96.46%, and the average rate of producing CO2 emissions is 70.39 kg/yr. The analysis results of turbulence intensity parameter shows that 23 stations in Afghanistan have a low turbulence intensity which means the mechanical components of wind turbines are under low fatigue loads and the rest of stations have a medium turbulence intensity which means the mechanical components of wind turbines are under medium fatigue loads. The highest and lowest values of turbulence intensity parameter are associated with Sindand and Qunduz stations respectively. Given the apparent and urgent need to develop renewable energy sources in Afghanistan, results of the present study, in addition to improving people's lives, could accompany economic development.

The objective of this study is to socially and economically investigate installation of wind turbines in Iran in order to produce hydrogen using its electricity. Due to adequate condition of wind blow in Manjil, Zabol, and Ardebil, these cities were chosen as the case studies and sample society. To scrutinize the acceptance of wind power among the sample members, first, data was gathered through questionnaires and analyzed by Partial Least Squares (PLS) approach to Structural Equations Modeling (SEM). The results showed that the first hypothesis (H1), positive effect of financial condition of households on wind power acceptance, was rejected with the coefficient of 1.184. On the other hand, the second hypothesis (H2) was approved with a meaningful coefficient of 3.159, that is, the social awareness of wind energy has a positive effect on its acceptance as an electricity generating source. Costs and Incomes Chart was utilized to estimate the payback period of investing in the wind power site project. Five wind turbines with different nominal capacities were tested and the results showed that the payback period for Manjil is shorter than that of others’ in a way that for turbines with 5, 30, 50, 60 and 100 kW nominal capacity is 3.1, 2.4, 2.3, 1.9, 2.6 years respectively. Finally, Hummer H25.0–60 KW wind turbine was selected due to its payback time which was less than other turbines to estimate the amount of hydrogen produced. The results showed that with installing one set of this turbine in Manjil, Zabol, and Ardebil 7.12, 5.82 and 5.72 ton hydrogen per year will be produced, respectively.

Wind energy is becoming one of the most important sources of renewable energy for many countries in the future. The purpose of this study is to predict wind speed using different algorithms. In this study, a new hybrid algorithm is developed to predict the wind speed behavior, and 24 h predictions of changes in wind speed are obtained with the aid of Homer software. The proposed algorithm is a combination of a well-known artificial neural network predictor called extreme learning machine as an artificial neural network algorithm and the Grey model (1, 1) as a method of Grey systems theory. Long-term wind speed forecasts are obtained using three-year data (2013–2016) of eight variables: TMAX, TMIN, VP, RHMIN, RHMAX, WINDSPEED, SUNSHINE HOURS, and PERCIPITATION for the Zanjan city in Iran, and 24 h wind speed forecast is obtained using 10-year data (2005–2015) pertaining to this city. The results show that proposed algorithm with relative measure of fit R2 of 0.99376 and mean square error of 0.000376 provides better predictions of wind speed in the study area than ordinary extreme learning machine algorithm with R2 of 0.98075 and mean square error of 0.00720. Also, the 24 h prediction of changes in wind speed is done using Homer software. The methodology in this research is more efficient in terms of execution performance and accuracy.

Maximization of the incident energy at surfaces of the photovoltaic modules is among the key factors of energy extraction maximization. In this paper, fixed tilt solar-PV systems are considered. The incident solar energy maximization problem is formulated as the maximization of the solar energy incident on the flat surfaces of the traditional flat solar-PV modules. Therefore, the presented study is not applicable to the curved solar-PV modules. The monthly, seasonally, semi-annually, and annually fixed tilt alternatives have been tested, then the optimal alternative has been selected. The Sahara middle (Adrar) district is considered as a representative site at the Algerian Big South as the long-term meteorological data are available from the ground meteorological station there. In comparison with the horizontally placed solar-PV modules, it is found that the incident solar energy increased by 20.61% for monthly, 19.58% for seasonal, 19.24% for semi-annual, and 13.78% for yearly adjustments. The results obtained with the ground measurements are compared with satellite measurements. It is found that the semi-annual adjustments in the tilt, which is estimated at 3.50° for the warm period (April – September) and 49.20° for the cold period (October – March) is the optimal compromise choice for the selected region.