Nonlinear relationship between household composition and electricity consumption: optimal threshold models

Huang, Wen‐Nan

doi:10.1007/s11081-022-09732-5

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…The availability of electrical energy is a critical factor in satisfying the requirements of sustainable development. The study by Huang (2022) investigates the nonlinear relationship between household composition and residential electricity consumption. A threshold model is used in which household size and electricity consumption are the threshold variables.…”

Section: Overview Of the Special Issue Articlesmentioning

confidence: 99%

Introductory remarks on the special issue of Optimization and Engineering dedicated to SDEWES 2021 conference

et al. 2022

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The background of this Special Issue (SI) of the Optimization and Engineering (OPTE) journal is the 2021 Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conference. This broad field was discussed by the participants of the 16th SDEWES Conference held in Dubrovnik, Croatia. This SI aims to bring together articles that discuss recent advances in optimization methods and algorithms that integrate various life-supporting systems. A decision-making problem often appears as a parameter estimation problem; it can be formulated as an optimization model and solved using different optimization algorithms and techniques. All papers included in this OPTE journal SI consider aspects of numerical analysis, mathematical modelling, and computational methods involved in investigating, planning and implementing sustainable development. In this context, the guest editors have confidence that the selected papers and addressed issues will substantially contribute to the increase of the knowledge body published in the OPTE journal, and the SI will be a powerful platform for researchers to discuss, share, and disseminate new ideas.

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Section: Overview Of the Special Issue Articlesmentioning

confidence: 99%

Introductory remarks on the special issue of Optimization and Engineering dedicated to SDEWES 2021 conference

et al. 2022

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A SHAP machine learning-based study of factors influencing urban residents' electricity consumption - evidence from chinese provincial data

Wang,

Hu,

Hou

et al. 2024

Environ Dev Sustain

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Optimization of the composition of residential buildings in a renewable energy community based on monitored data

Schito,

Taverni,

Conti

et al. 2024

Optim Eng

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Energy communities (ECs) are a promising solution to integrate renewable local production with buildings’ systems and services. To exploit renewable energy sources, ECs should be carefully designed, identifying an appropriate mix of prosumers and consumers. In this research, the electrical energy loads of eight dwellings have been monitored for a year. Then, each dwelling is evaluated either as a mere consumer, maintaining its monitored electrical consumption profile as it is, or as a prosumer, thus simulating a photovoltaic system on the roof, sized to provide a given fraction of its energy needs and sharing the surplus with other EC participants. Genetic optimization is employed to seek the optimal mix of consumers and prosumers within the community to optimize the shared energy within the EC. Results show that dwellings with night-time energy requirements are included as prosumers to maximize photovoltaic power sharing during daylight time, and dwellings with regular daily loads are included as consumers.

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Nonlinear relationship between household composition and electricity consumption: optimal threshold models

Cited by 5 publications

References 43 publications

Introductory remarks on the special issue of Optimization and Engineering dedicated to SDEWES 2021 conference

Introductory remarks on the special issue of Optimization and Engineering dedicated to SDEWES 2021 conference

A SHAP machine learning-based study of factors influencing urban residents' electricity consumption - evidence from chinese provincial data

Optimization of the composition of residential buildings in a renewable energy community based on monitored data

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