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This paper proposes an efficient energy management approach for managing the demand response and energy forecasting in a smart grid using Internet of Things (IoT). The proposed energy management approach is the hybrid technique that is the joint execution of adaptive neuro fuzzy inference system (ANFIS) and balancing composite motion optimization (BCMO), thus it is called ANFIS-BCMO technique. An energy management approach is developed using price-based demand response (DR) program for IoT-enabled residential buildings. Then, we devised a approach depends on ANFIS-BCMO technique to systematically manage the energy use of smart devices in IoT-enabled residential buildings by programming to relieve peak-to-average ratio (PAR), diminish electricity cost, and increase user comfort (UC). This maximizes effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings on smart cities. The ANFIS-BCMO technique automatically responds to price-based DR programs to combat the main problem of DR programs that is the limitation of the consumer's knowledge to respond when receiving DR signals. For consumers, the proposed ANFIS-BCMO based strategy programs appliances to exploit benefit based on reduced electricity bill. By then, the proposed method increases the stability of the electrical system by smoothing the demand curve. At last, the proposed model is executed on MATLAB/Simulink platform and the proposed method is compared with existing systems. [ABSTRACT FROM AUTHOR]

In this paper, optimum transistor sizing of CMOS differential amplifier using tunicate swarm algorithm (TSA) is proposed. The designing of CMOS differential amplifier is activated to determine the best feasible design parameter values. This work proposes the optimized values of various parameters of a CMOS differential amplifier for better performance. TSA is chosen to optimize the circuit area. TSA has the ability to solve complex functions, like MOS transistor size and bias current. TSA is employed to optimize the parameters of circuit design, like area, power dissipation MOS transistor size, and also used to enhance other circuit specifications, while fulfilling circuit design criteria. The design objectives of CMOS differential amplifier are considered the fitness function of TSA algorithm. Then, weight parameters of CMOS differential amplifier design are optimized using TSA. By CMOS differential amplifier using TSA algorithm, we can optimize circuit design parameters with higher probability of yielding optimal results regarding circuit area lessening, lesser power dissipation and MOS transistor sizes. The proposed method is implemented in the MATLAB platform. The proposed CMOS-DA-TSA method attains 52.01%, 50.29% and 44.30% minimum slew rate, 64.61%, 75.30% and 55.92% minimum power dissipation compared to the existing methods like CMOS-ACD-SOA, CMOS-PAI-FOPSO and CMOS-PSO-MOL, respectively. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach for solving the unit commitment problemusing an hybrid approach based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Unit Commitment Problem (UCP) used for achieving minimum operating cost in the scheduling operation of power system generating units subjected to needed demand and reserve constraints is a nonlinear mixed integer optimization problem. The on/off state of the generating units at each hour interval of the planning period that optimally transmits load and reserve among the committed units is determined by the UC problem. The significance of the need for a more competent optimal solution to the UCP problem is increasing with the routinely changing demand. To accomplish this, hereby, we propose a hybrid approach that yields optimal commitment of the units by solving the unit commitment problem subjected to essential constraints. To verify the effectiveness of the proposed method numerical studies have been performed for the NTPS 7 unit system for a period of 24 hours. The results show that the proposed method outperforms the other state-of-the-art algorithms and the conventional methods in solving the unit commitment problems. [ABSTRACT FROM AUTHOR]