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In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA. [ABSTRACT FROM AUTHOR]

Despite the fact that research on multigeneration and polygeneration systems are available, more efficient and effective systems that fulfill several energy demands at the same time are still needed. Furthermore, efficient use and harvesting of renewable energy sources are critical for global emission reductions. Accordingly, this paper proposes a novel geothermal-solar-based hybrid system for simultaneous power and freshwater production. The overall objective of the study is to model the proposed cogeneration system with energy and exergy exergoeconomic, and economic viewpoints, and obtain the optimum performances of the system using non-dominated sorting genetic algorithm II (NSGA-II) method and LINMAP decision-making approach. According to the obtained results, at the base operating conditions, the exergy destruction rate for the CPVT is the highest with 311.3 k W amongst other components, followed by the RO unit with a value of 159 k W. Also, the net output electricity of the total system and freshwater generation rate are computed to be 775.3 k W and 10.14 k g. s − 1 . Also, for the first optimization scenario (S U C P − η e x ), the exergy efficiency and sum unit cost of the products are obtained by 42.31 % and 16.32 $. G J − 1 , respectively. And, the produced electricity and freshwater are computed to be 755.3 k W and. 16.44 k g. s − 1 • Proposal and study of a newly designed electricity/freshwater co-production system. • Smart use of solar and geothermal energies for the performance enhancement. • Energy, exergy, exergy costing, and NPV methods together with NSGA-II optimization. • The optimum exergy efficiency and unit cost are separately 42.31% and 16.32 $/GJ. • The optimum payback period of the system is estimated to be 1.07 years. [ABSTRACT FROM AUTHOR]

In this study, comprehensive energy, exergy, and economic modeling, and optimization of a flash-binary geothermal system, were carried out for power and hydrogen production. The binary system is a combination of a dual-pressure organic Rankine cycle and proton exchange membrane electrolyzer. For performance improvement of dual-pressure organic Rankine cycle, zeotropic mixtures were used as the working fluids. The multi-objective Genetic algorithm was employed to optimize the system performance. According to the obtained results, the highest rate of exergy destruction was obtained for the steam turbine with a value of 16.42 kW. For the base case simulation, the energy and exergy efficiencies of the proposed system were obtained by 16.66 % and 58.03 % , respectively; also, the generated power, overall exergy destruction, and hydrogen production capacity were calculated by 114.53 kW , 82.755 kW , and 0.306 kg · hr − 1 , respectively. For the multi-objective optimization mode, the best performance was calculated for Pentane (0.467)/Butane (0.533) with energy and exergy efficiencies and hydrogen production of 16.67 % , 58.14 % , and 0.37 kg · hr − 1 . Moreover, the optimized payback period and NPV were around 5.52 years and 402600 $ , respectively. • Proposal of a flash-binary geothermal system for power and hydrogen production. • Using zeotropic mixtures for performance improvement of dual-pressure ORC. • Analyzing economic aspects of the proposed system for probable investment. • Energy and exergy efficiencies of 16.67 % and 58.14 % for the optimized mode. • Optimized payback period and total profit of 5.52 years and 402800 $. [ABSTRACT FROM AUTHOR]

In power systems protection, the optimal coordination of directional overcurrent relays (DOCRs) is of paramount importance. The coordination of DOCRs in a multi-loop power system is formulated as an optimization problem. The main objective of this paper is to develop the whale optimization algorithm (WOA) for the optimal coordination of DOCRs and minimize the sum of the operating times of all primary relays. The WOA is inspired by the bubble-net hunting strategy of humpback whales which leads toward global minima. The proposed algorithm has been applied to six IEEE test systems including the IEEE three-bus, eight-bus, nine-bus, 14-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed WOA are compared with those obtained by other up-to-date algorithms. The obtained results show the effectiveness of the proposed WOA to minimize the relay operating time for the optimal coordination of DOCRs. [ABSTRACT FROM AUTHOR]

In this paper, a novel geothermal-based trigeneration system producing electricity, cooling, and freshwater is devised. The whole setup comprises a flash-binary geothermal cycle, an ejector refrigeration cycle, a dual-pressure organic Rankine cycle, and a reverse osmosis unit. In the dual-pressure organic Rankine cycle, the utilization of the zeotropic mixture is examined for performance improvement. The overall system is analyzed from energy, exergy, and economic viewpoints. Likewise, the system is inspected by the thermodynamic and economic sensitivity analyses based on the best working fluid. By specifying the Butene/Isopentane zeotropic mixture as the best working fluid, it is revealed that from 10932 k W of the input exergy to the system, 5561.74 k W is destructed within different components. In this way, the recovered exergy rate by net output electricity is equal to 3633.33 k W , by cooling load is 1663.44 k W , and by freshwater is 104 k W. Also, the optimum net output power and total energy and exergy efficiencies were obtained to be 3.65 MW , 25.06 % , and 35.06 % , correspondingly. In this situation, the sum unit cost of products and payback period of the system were 14.50 $. GJ − 1 and 3.66 years , respectively. • Energy, exergy, and economic (exergoeconomic + NPV) analyses of a new geothermal system. • Integration of a flash-binary geothermal cycle with an ERC, a DORC, and a RO unit. • Performance enhancement by determining the best zeotropic working fluid of the DORC. • Optimum working fluid has been predicted as Butene (0.442)/Isopentane (0.558). • Optimum exergy efficiency and payback period are 35.06% and 3.66 years, separately. [ABSTRACT FROM AUTHOR]

The current work is a study of electricity, hydrogen, and freshwater polygeneration system fueled by biomass fuel. Accordingly, innovative integration of a Rankine cycle, a multi-effect desalination, and a solid oxide electrolyzer cell has been considered utilizing a syngas production biomass combustion chamber. The waste heat and a part of the output electricity of the Rankine cycle have been employed to launch the desalination and electrolyzer units, respectively. The suggested polygeneration is analyzed from the thermodynamic and exergoeconomic viewpoints through developing a code in engineering equation solver software and a multi-criteria optimization via MATLAB software. Hence, the NSGA-II optimization method and decision-making TOPSIS technique have been implemented. The parametric study has been conducted based on the effect of biomass fuel mass flow rate, turbine inlet pressure, combustion chamber outlet temperature, and pinch point temperature difference of the steam generator on the thermodynamic and exergoeconomic variables of the whole system. Considering the total exergy destruction rate, exergy efficiency, and total unit exergy cost of products as objective functions, the suggested system achieved the optimum values of 17.64%, 7658.5 kW , and 26 $ / GJ corresponding for these variables. • An integrated cycle for power, freshwater, and hydrogen is devised. • Comprehensive energy, exergy, exergoeconomic analyses are performed. • Multi-objective optimization is developed for the optimization process. • For the optimization scenario, 17.16%, 7658.5 kW, and 26 $/GJ for exergy efficiency, exergy destruction, and unit exergy cost were obtained. [ABSTRACT FROM AUTHOR]

A novel power and freshwater generation system is presented based on the gas turbine cycle as the main system and Kalina cycle and humidification-dehumidification desalination unit as the waste heat recovery subsystems. To evaluate the system's performance, energy, exergy, economic, and environmental analysis is performed. The optimum performance of all optimization scenarios is found by applying the multi-objective genetic algorithm and using the technique for order of preference by similarity to ideal solution (TOPSIS) method. The sensitivity analysis is also performed to assess the effect of various parameters on the system's performance. By using Net present value, the feasibility of the plant for the construction from the economic viewpoint is analyzed. Based on the base case results, the performance metrics are evaluated as the energy efficiency of 0.9398 , exergy efficiency of 43.11 % , sum unit cost of 19.44 $. GJ − 1 , Levelized total emission of 63571 kg. kW − 1 , and freshwater production rate of 10.39 kg. s − 1 . Among all system components, the combustion chamber is contributed to the highest rate of exergy destruction rate by 16544 kJ. s − 1 . For fuel cost of 3 $. GJ − 1 and electricity price of 0.09 $. kWh − 1 , the total net present value for the plant lifetime is obtained 1.736 × 10 7 $ , which means that the plant is feasible for construction from the economic perspective. Based on the optimization results, the maximum value of exergy efficiency and minimum value of Levelized total emission are obtained in LTE-ε-TGOR scenario by 43.84 % and 62602 kg. kW − 1 , respectively. • A novel power and water production system based on gas turbine cycle is proposed. • Thermodynamic, economic, and environmental analyses are performed. • Payback period and net present value techniques are employed for economic analysis. • Multi-objective genetic algorithm is used to optimize the performances of the system. • TOPSIS decision-making method is applied to find the final optimum solutions. [ABSTRACT FROM AUTHOR]

The fluctuation of the wind speed and direction is due to the stochastic nature of the wind and the enforcing atmospheric pressure. Accordingly, the output power forecasting of the wind farms (WFs) will be difficult. In this paper, a novel method based on Gaussian Processes (GPs) is proposed to improve the probabilistic prediction of the WF levels and regional WFs. The Covariance Functions (CFs) are the key ingredient in using GPs. Thus, different groupings of CFs are investigated comprehensively. The GP includes different types as dynamic, static, direct, indirect, and combined structures, which are investigated in this study. The results of comparison between dynamic and static GP, reveal that the dynamic GP generates keen Prediction Intervals (PIs). In addition, with comparing the accuracy of direct and indirect prediction plan, it shows that indirect prediction strategy brings about wider PIs. The various evaluation metrics have applied to benchmark the different methods performance, and its results show that the indirect–dynamic GP has better performance than other combined structures of GP as well as other methods, in both WF levels and regional WFs, while its maximum error has obtained as about 5% less than others. Moreover, the proposed model provides precise results of forecasted energy in every time steps in both deterministic and probabilistic wind power forecasting. The compared results between indirect–dynamic GP and other structures show the highest average coverage error, about 1% and 2.2% higher in the regional level and WF levels, respectively, the lowest prediction interval nominalized average width, about 5% and 15% lower in the regional level and WF levels, respectively, and the highest interval sharpness, about 2% and 5% higher in the regional level and WF levels, respectively. [ABSTRACT FROM AUTHOR]

In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA) is implanted into the original particle swarm algorithm (PSO) and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO) are compared with other evolutionary optimization algorithms (EA) and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution. [ABSTRACT FROM AUTHOR]