A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home

Makhadmeh, Sharif Naser; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Naim, Syibrah; Abasi, Ammar Kamal; Alyasseri, Zaid Abdi Alkareem

doi:10.1016/j.swevo.2020.100793

Cited by 73 publications

(29 citation statements)

References 49 publications

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“…where l 1 denotes the LOC of the first SA, and l m is the LOC of the last SA. Furthermore, starting and ending time of SAs operations are presented in vectors St and Et, respectively, as shown in Equations (8) and (9). The aforementioned time parameters are illustrated in Figure 1.…”

Section: Power Consumptionmentioning

confidence: 99%

“…Furthermore, this kind of optimization provides benefits for users, such as reducing electricity bill (EB) and improving their comfort level. However, the power demand of users can be optimized by scheduling the operations of appliances in a smart home or Internet-of-Things at suitable periods in accordance with a dynamic price scheme(s) (i.e., the electricity prices vary dynamically over time) [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…These pricing schemes are generated to incentivize users to shift most of their home appliances operation time from peak periods to off-peak periods [6]. The most popular dynamic pricing schemes that can be generated by PSCs are real-time price (RTP), time-of-use price (TOU), critical period price (CPP), and inclining block rate (IBR) [9,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

et al. 2021

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The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

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Section: Power Consumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

et al. 2021

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“…As the second greatest wolf in the cluster, the β wolf is perhaps most likely to become an α leader. The third degree of the gray wolves, δ wolves, annihilates the wolves in the front, and the last degree is called the ω wolves, who ensure the perceived safety and the competence of the wolf packs [114]. The flowchart of this algorithm is shown in Figure 4.…”

Section: Grey Wolf Algorithmmentioning

confidence: 99%

“…The market participants cannot change their operation structures one hour before real time. Proposing the suitable forecasting horizons with considering the market time-line and the participants 'ability has not been investigated in the literature yet, systematically [114].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm

et al. 2021

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This paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best data with the highest relevancy are selected, using a feature selection algorithm. The second part is based on a nonlinear support vector network and auto-regressive integrated moving average (ARIMA) method for better training the previous values of electricity prices. The third part optimally adjusts the proposed support vector machine parameters with an error-base objective function, using the improved grey wolf and particle swarm optimization. The proposed method is applied to forecast electricity markets, and the results obtained are analyzed with the help of the criteria based on the forecast errors. The results demonstrate the high accuracy in the MAPE index of forecasting the electricity price, which is about 91% as compared to other forecasting methods.

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A novel approach for nature‐based optimization algorithms: The threat factor approach

Toz

2021

Concurrency and Computation

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Nature-inspired optimization algorithms especially those based on the hunting behaviors of the creatures assume that the hunting operations are performed in a safe environment. However, generally, there are threats in real-life for the hunter-animals. This paper focuses on these threat factors and proposes that they can be used to improve the searching abilities of the algorithms. Gray wolf optimization (GWO) algorithm was selected to present the proposed approach and it was assumed that there was a mountain lion as the threat factor living in the same habitat with the wolf pack. The relations between the two predators were modeled and used to improve the performance of the algorithm. Five experiments were conducted to test the performance of the proposed method and the results were compared with the GWO and four optimization algorithms from the literature. It is shown that the proposed algorithm obtained best results for 21 of the 50 benchmark functions, while its closest competitor achieved the best results for 16 functions. Besides, the results of the Wilcoxon signed-rank test indicated that the proposed method is superior to all other methods. In addition, it was shown that the threat factor approach does not cause a significant increase in the processing time. K E Y W O R D SGWO algorithm, nature-inspired optimization, threat factor approach INTRODUCTIONNature is a great source for modeling optimization algorithms since there are so many creatures, events, or any other phenomena that can be used as the base for these algorithms. Therefore, nature-inspired optimization algorithms have been very popular over the past three decades.Generally, these algorithms can be divided into four classes as evolutionary-based, swarm-based, physics-based, and human-based algorithms. 1Evolutionary-based algorithms model natural evolution concepts such as mutation and natural selection. These algorithms are based on the rule of eliminating weak entities while providing that the strong individuals pass to the next generation. Genetic algorithm 2 which was proposed by Holland is the best-known evolutionary algorithm. differential evolution (DE), 3 genetic programming, 4 learner performance based behavior 5 algorithm, and biogeography-based optimizer 6 are some of the other algorithms based on the evolutionary approach. Swarm-based optimization algorithms are derived from the behaviors of the herds of the creatures to reach the food. Particle swarm optimization (PSO) is the most-known swarm-based optimization algorithm. PSO, simply, mimics the searching-food behaviors of a swarm of fish or birds. 7 Ant colony optimization, 8 artificial bee colony, 9gray wolf optimization (GWO), 10 ant lion optimization (ALO), 11 whale optimization algorithm (WOA), 12 dragonfly algorithm. 13 Moth-flame optimization (MFO), 14 salp swarm algorithm, 15 fish-swarm algorithm, 16 bird mating optimizer, 17 bat-inspired algorithm, 18 firefly algorithm, 19 naked mole-rat algorithm, 20 cuckoo search algorithm, 21 donkey and smuggler optimization, 22 and fit...

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A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home

Cited by 73 publications

References 49 publications

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

Forecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm

A novel approach for nature‐based optimization algorithms: The threat factor approach

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