A hybrid intelligent predicting model for exploring household CO2 emissions mitigation strategies derived from butterfly optimization algorithm

Wen, Lei; Cao, Yang

doi:10.1016/j.scitotenv.2020.138572

Cited by 36 publications

(15 citation statements)

References 35 publications

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“…The authors recommend making tax-free energy vehicles, subside energy efficient devices (e.g., air conditioners, solar water heaters, and electric cars), and educate people to have environmental consciousness. A similar study by Wen and Cao [53] predicts residential CO 2 emissions in a China region. In this case, the authors propose applying purchase subsidies to promote the acquisition of energy efficiency appliances, stimulation of research on energy efficiency appliances, and enhancement in the understanding of carbon reduction needs.…”

Section: Market-basedmentioning

confidence: 60%

“…The study concludes that BAs, SECs, and occupant behavior influence the energy consumption of appliances. Likewise, Wen and Cao, in the following papers [53,55], present two predictive models to analyze CO 2 emissions and their influence factors in the residential sector. The authors use an innovative combination of techniques (e.g., bi-variate correlation analysis, kernel principal component analysis, butterfly optimization algorithm, and least square support vector machine) to perform the CO 2 emissions prediction.…”

Section: Ddh Models With Other Methodologiesmentioning

confidence: 99%

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The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

et al. 2021

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Bottom-up energy models are considered essential tools to support policy design of electricity end-use efficiency. However, in the literature, no study analyzes their contribution to support policy design of electricity end-use efficiency, the modeling techniques used to build them, and the policy instruments supported by them. This systematic review fills that gap by identifying the current capability of bottom-up energy models to support specific policy instruments. In the research, we review 192 publications from January 2015 to June 2020 to finally select 20 for further examination. The articles are analyzed quantitatively in terms of techniques, model characteristics, and applied policies. The findings of the study reveal that: (1) bottom-up energy models contribute to the support of policy design of electricity end-use efficiency with the application of specific best practices (2) bottom-up energy models do not provide a portfolio of analytical methods which constraint their capability to support policy design (3) bottom-up energy models for residential buildings have limited policy support and (4) bottom-up energy models’ design reveals a lack of inclusion of key energy efficiency metrics to support decision-making. This study’s findings can help researchers and energy modelers address these limitations and create new models following best practices.

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Section: Market-basedmentioning

confidence: 60%

Section: Ddh Models With Other Methodologiesmentioning

confidence: 99%

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

et al. 2021

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“…[42] used the BOA for regression test suite optimisation problem. [43] improved BOA by using a chaotic Tent map for population initialisation and adaptive weight, then applied it for finding the optimal parameter settings for the least square support vector machine (LSSVM) and used the improved algorithm for the household CO2 emissions problem. [44] improved BOA to be used in Capacitated Vehicle Routing Problem (CVRP), where they applied intra route operators of CVRP, including move operator, swap operator, and inversion operator as local search operators in BOA.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

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“…According to the theorem "there is no free lunch", each algorithm has its own advantages [41,42], and the BOA has been proposed and applied in some aspects, for example, Zhi et al [43] proposed an improved version of BOA to optimize the performance of the heat recovery system, and the results illustrated that high relative humidity, the pressure of inlet gases, and low operating temperature develop GHG emission reduction and system exergy performance. Wen et al [44] used a newly proposed hybrid model (hereafter referred to as EBOA-LSSVM) to predict residential CO2 emissions in the Yangtze River Delta region. The final simulation results demonstrate the outstanding performance of the new model by comparing its predictive accuracy with other models.…”

Section: A Motivation To Improve Boamentioning

confidence: 99%

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

et al. 2020

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In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated. Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury. The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury. As the optimization core, we utilized the Butterfly optimization algorithm (BOA). Up to now, few improved variants of BOA have been reported. The original BOA converges slowly and quickly falls into a locally optimal solution. An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA. Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions. Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance. The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM. We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury. The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.

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A hybrid intelligent predicting model for exploring household CO2 emissions mitigation strategies derived from butterfly optimization algorithm

Cited by 36 publications

References 35 publications

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

Dynamic Butterfly Optimization Algorithm for Feature Selection

Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms

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