Probabilistic energy consumption analysis in buildings using point estimate method

Bordbari, Mohammad Javad; Seifi, Ali Reza; Rastegar, Mohammad

doi:10.1016/j.energy.2017.10.091

Cited by 44 publications

(28 citation statements)

References 38 publications

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“…PEM is one of the methods used to estimate the value of uncertain parameters with high computational accuracy and is based on the concept of uncertain input parameters moments [65]. This method covers uncertainties by establishing a connection between input and output variables whose main steps are explained by Hong [66].…”

Section: Point Estimate Methods (Pem)mentioning

confidence: 99%

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

et al. 2019

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Aggregation of distributed generations (DGs) along with energy storage systems (ESSs) and controllable loads near power consumers has led to the concept of microgrids. However, the uncertain nature of renewable energy sources such as wind and photovoltaic generations, market prices and loads has led to difficulties in ensuring power quality and in balancing generation and consumption. To tackle these problems, microgrids should be managed by an energy management system (EMS) that facilitates the minimization of operational costs, emissions and peak loads while satisfying the microgrid technical constraints. Over the past years, microgrids' EMS have been studied from different perspectives and have recently attracted considerable attention of researchers. To this end, in this paper a classification and a survey of EMSs has been carried out from a new point of view. EMSs have been classified into four categories based on the kind of the reserve system being used, including non-renewable, ESS, demand-side management (DSM) and hybrid systems. Moreover, using recent literature, EMSs have been reviewed in terms of uncertainty modeling techniques, objective functions (OFs) and constraints, optimization techniques, and simulation and experimental results presented in the literature.

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Section: Point Estimate Methods (Pem)mentioning

confidence: 99%

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

et al. 2019

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“…The authors in [11] conducted an analysis of energy consumption in buildings using two-point estimation method in order to model the structural uncertainties of structural and environmental parameters.…”

Section: Energy Analytics Methods: a Literature Reviewmentioning

confidence: 99%

Modelling and evaluation of the Baseline Energy Consumption and the Key Performance Indicators in Technical University of Cluj‐Napoca buildings within a Demand Response programme: a case study

Cretu

Czumbil

Bargauan

et al. 2020

IET Renewable Power Generation

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“…Optimal bandwidth h M is what made ISE ĥ z (z) the smallest. In order to simplify the calculation, we performed some approximate processing for Equation (5). Firstly, the last item h 2 z (z) was independent from h M ; thus, we focused on the first two items of ISE ĥ z (z) .…”

Section: Reconstruction Of Optimal Important Distribution Function Bamentioning

confidence: 99%

“…The approximate method generally reduces repeated sampling with mathematical methods. The point estimation method (PEM) [5] is a typical approximate method, and is usually classified into two-point estimation [6] and three-point estimation [7]. Though PEM can be the fastest among the various PLF methods in small-scale calculations, its error is high in large-scale power grids.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling

Wang

Rong

2018

Energies

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The growing amount of distributed generation has brought great uncertainty to power grids. Traditional probabilistic load flow (PLF) algorithms, such as the Monte-Carlo method (MCM), can no longer meet the needs of efficiency and accuracy in large-scale power grids. Latin Hypercube Sampling (LHS) develops a sampling efficiency and solves the correlation problem of distributed generation (DG) access nodes for accuracy analyses. In this paper, a modified Latin Hypercube-Important Sampling method is proposed for higher efficiency and precision by using the importance sampling method before LHS and the Cholesky decomposition method in correlation calculations. The simulation results are presented using a modified IEEE 30-bus system and are compared with traditional MCM and LHS.

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Probabilistic energy consumption analysis in buildings using point estimate method

Cited by 44 publications

References 38 publications

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

Modelling and evaluation of the Baseline Energy Consumption and the Key Performance Indicators in Technical University of Cluj‐Napoca buildings within a Demand Response programme: a case study

Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling

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