Building energy optimization using Grey Wolf Optimizer (GWO)

Ghalambaz, Mehdi; Yengejeh, Reza Jalilzadeh; Davami, Amir Hossein

doi:10.1016/j.csite.2021.101250

Cited by 86 publications

(34 citation statements)

References 34 publications

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“…It is also simple, easy to use, flexible, extensible, and easy to implement (Mirjalili et al, 2014 ; Faris et al, 2018 ). Past studies have shown the capability of the GWO for optimizing problems of energy prediction and optimizing the parameters of the grey model (Ghalambaz et al, 2021 ; Kong & Ma, 2018 ; Tian et al, 2020 ; Xie et al, 2021c ). Thus, it is suitable for optimizing the parameters of the proposed ANDGM(1,N) model.…”

Section: Proposed Prediction Modelmentioning

confidence: 99%

A new multivariate grey prediction model for forecasting China’s regional energy consumption

Chiu

et al. 2022

Environ Dev Sustain

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Predicting energy consumption is an essential part of energy planning and management. The reliable prediction of regional energy consumption is crucial for the authority in China to formulate policies by with respect to the dual control of its energy consumption and energy intensity. Given that energy consumption is affected by a number of factors, this study proposes a non-homogeneous, discrete, multivariate grey prediction model based on adjacent accumulation to predict the regional energy consumption in China. Interestingly regional GDP was selected by grey relational analysis as the independent variable in the proposed model. The results show that it can outperform the other multivariate grey models considered in terms of predicting regional energy consumption in China. Moreover, we found that economic development and energy consumption of each region in China remain closely related. In the post-COVID-19 period, regional economic development will continue to grow and increase energy consumption.

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Section: Proposed Prediction Modelmentioning

confidence: 99%

A new multivariate grey prediction model for forecasting China’s regional energy consumption

Chiu

et al. 2022

Environ Dev Sustain

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“…When , trends to 0 and then has no action. Therefore, is mapped into the distance of grasshoppers in the interval of 1 , 4 . Thus the space between two grasshopper is divided into repulsion region, comfort zone, and attraction region.…”

Section: Improved Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…Step 4 . For every grasshopper, the distance between the grasshoppers is firstly mapped into the interval 1 , 4 , and the selected probability is adopted. If , the position of the grasshopper is updated by use of Eq.…”

Section: Improved Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…Based on the existence of the constraint conditions, optimization models are divided into unconstrained optimization models and constrained optimization models, which widely exist in computer science 1 , artificial intelligence 2 , pattern recognition 3 , energy consumption 4 , truss structural systems 5 , engineering areas 6 , nonlinear time series 7 , and so on.…”

Section: Introductionmentioning

confidence: 99%

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The improved grasshopper optimization algorithm and its applications

Qin

Yang

2021

Sci Rep

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Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.

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“…Therefore, the GWO algorithm has good problem-solving accuracy and convergence speed. GWO is widely used in engineering design [46,47], path planning [48,49], electric load forecasting [50], building energy optimization [51], feature subset selection [52], image processing [53][54][55], and multiple hydropower reservoir operations [56,57].…”

Section: Introductionmentioning

confidence: 99%

Application of AMOGWO in Multi-Objective Optimal Allocation of Water Resources in Handan, China

Yan

Sha

et al. 2021

Water

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The reasonable allocation of water resources using different optimization technologies has received extensive attention. However, not all optimization algorithms are suitable for solving this problem because of its complexity. In this study, we applied an ameliorative multi-objective gray wolf optimizer (AMOGWO) to the problem. For AMOGWO, which is based on the multi-objective gray wolf optimizer, we improved the distance control parameter calculation method, added crowding degree for the archive, and optimized the selection mechanism for leader wolves. Subsequently, AMOGWO was used to solve the multi-objective optimal allocation of water resources in Handan, China, for 2035, with the maximum economic benefit and minimum social water shortage used as objective functions. The optimal results obtained indicate a total water demand in Handan of 2740.43 × 106 m3, total water distribution of 2442.23 × 106 m3, and water shortage of 298.20 × 106 m3, which is consistent with the principles of water resource utilization in Handan. Furthermore, comparison results indicate that AMOGWO has substantially enhanced convergence rates and precision compared to the non-dominated sorting genetic algorithm II and the multi-objective particle swarm optimization algorithm, demonstrating relatively high reliability and applicability. This study thus provides a new method for solving the multi-objective optimal allocation of water resources.

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Building energy optimization using Grey Wolf Optimizer (GWO)

Cited by 86 publications

References 34 publications

A new multivariate grey prediction model for forecasting China’s regional energy consumption

A new multivariate grey prediction model for forecasting China’s regional energy consumption

The improved grasshopper optimization algorithm and its applications

Application of AMOGWO in Multi-Objective Optimal Allocation of Water Resources in Handan, China

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