Papers by Mr. Saad Rashid
Applied Sciences
The reptile search algorithm is a newly developed optimization technique that can efficiently sol... more The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviat...
An Energy Efficient Scheduling of a Smart Home Based on Optimization Techniques
Advances in intelligent systems and computing, Jun 8, 2018
After the introduction of smart grid in power system, two-way communication is now possible which... more After the introduction of smart grid in power system, two-way communication is now possible which helps in optimizing the energy consumption of consumers. To optimize the energy consumption on the consumer side, demand side management is used. In this paper, we focused on the optimization of smart home appliances with the help of optimization techniques. Cuckoo search algorithm, earthworm optimization and a hybrid technique cuckoo-earthworm optimization are used for scheduling the smart home appliances. Home appliances are classified into three groups and real-time pricing scheme is used. Techniques are evaluated and a performance comparison is performed. Results show that the proposed hybrid technique has decreased the electricity cost by 49% as compared to unscheduled cost and a trade-off exists between electricity cost and user comfort.
An Energy Efficient Scheduling of a Smart Home Based on Optimization Techniques
Innovative Mobile and Internet Services in Ubiquitous Computing, 2018
After the introduction of smart grid in power system, two-way communication is now possible which... more After the introduction of smart grid in power system, two-way communication is now possible which helps in optimizing the energy consumption of consumers. To optimize the energy consumption on the consumer side, demand side management is used. In this paper, we focused on the optimization of smart home appliances with the help of optimization techniques. Cuckoo search algorithm, earthworm optimization and a hybrid technique cuckoo-earthworm optimization are used for scheduling the smart home appliances. Home appliances are classified into three groups and real-time pricing scheme is used. Techniques are evaluated and a performance comparison is performed. Results show that the proposed hybrid technique has decreased the electricity cost by 49% as compared to unscheduled cost and a trade-off exists between electricity cost and user comfort.
Data-driven green energy extraction: Machine learning-based MPPT control with efficient fault detection method for the hybrid PV-TEG system
Energy Reports
Home Energy Management in Smart Grid Using Evolutionary Algorithms
2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), 2018
Home Energy Management Systems (HEMS) have been widely used for energy management in smart homes.... more Home Energy Management Systems (HEMS) have been widely used for energy management in smart homes. Energy management in a smart home is a challenging task, which require efficient scheduling of appliances. The main focus of HEMS is to schedule the operation of appliances in such a way that it gives us optimized performance in terms of Peak to Average Ratio (PAR), Electric Cost (EC) minimization, execution time and User Comfort (UC). The Time of Use (ToU) pricing scheme is used in this paper. We used Genetic Algorithm (GA), Biogeography-based optimization (BBO) and our proposed hybrid Genetic Biogeography-based Optimization (GBBO), techniques to schedule appliances in single home and for multiple homes. Simulations are carried out using eight different appliances. The results show that GA and GBBO execute better in case of PAR reduction and EC minimization. GBBO outperforms in terms of user comfort. We calculated the UC in terms of waiting time.
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Papers by Mr. Saad Rashid