Numerical Computation and Prediction of Electricity Consumption in Tobacco Industry

Laković, Mirjana; Pavlović, Ivan; Banjac, Miloš; Jović, Milica; Mančić, Marko

doi:10.22190/fume170927025l

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…Another popular measure is the pre-processing of data in order to eliminate outliers and noise, as well as isolate seasonal [113] or temperature related [174] patterns. For TSA and ML techniques this has been done by using wavelet or Fourier transformation in [39,58,90,108,111,120,[175][176][177][178][179][180]. Particular attention was payed to the prediction of special events and holidays in [118,[181][182][183].…”

Section: Measures For Improvement Of Accuracymentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Section: Measures For Improvement Of Accuracymentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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“…The part load performance of the other polygeneration modules are based on the manufacturer performance data: STC [52][53][54][55] VCC [58]. Modeling of electricity consumption was limited to the consumption for the purposes of running the modelled heating and ventilation system in addition to the polygeneration system, which accounts for most of the building electricity consumption, due to difficulties of prediction of electricity consumption during a typical year [57]. The simulation time step of 5 minute was adopted as recommended for better accuracy [5].…”

Section: Mathematical Modeling and Simulation Of The Energy Loads Of mentioning

confidence: 99%

Techno-economic optimization of configuration and capacity of a polygeneration system for the energy demands of a public swimming pool building

et al. 2018

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A polygeneration system is an energy system capable of providing multiple energy outputs to meet local demands, by application of high process integration. In this paper, optimal configuration and capacity of a polygeneration system for an indoor swimming pool building is determined by application of a method based on TRNSYS simulation and GenOpt optimization software. Based on the applicability, a superstructure of the polygeneration system is integrated, consisting of the following polygeneration modules: an internal combustion engine cogeneration module, a vapor compression chiller, and adsorption chiller, a ground source heat pump, flat plate solar thermal collectors, photovoltaic collectors, and heat storage. Annual behavior of energy loads of the public swimming pool building during a typical meteorological year and the polygeneration system are modeled and simulated using TRNSYS software, whereas techno-economic optimization is performed by GenOpt optimization. The results indicated the optimal configuration of the polygeneration system for the modelled energy demands, as well as the optimal capacity of the polygeneration modules, thus defining the optimal capacity of the polygeneration system for the energy demands of the public swimming pool building.

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“…The basic principle of modern engineering practice is an energy efficient design and operation [1][2][3]. Significant energy recovery is possible from ventilation equipment in the case of proper choice of equipment and operation [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…1. Recirculation of air, or cooling/heating with waste heat is not considered as heat recovery according to the interpretation of [2].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Energy Recovery Potentials in Ventilation Systems in Different Climates

et al. 2018

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The aim of this research study was to investigate the energy recovery potentials in ventilation systems under different climatic conditions. The well-known heating degree day from the literature was updated using the weather data of cities with different climates from the past 40 years. As the novelty of this research with the developed procedure drawn up in this study, the energetic possibilities of heat recovery under various climate and operating conditions may be examined in more detail and more realistically than with the methods and available information of current engineering practices. To achieve this long-term and high definition the weather data of several cities are processed in order to evaluate the possibilities of heat recovery on a daily and annual basis.

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Numerical Computation and Prediction of Electricity Consumption in Tobacco Industry

Abstract: Abstract. Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict

Cited by 6 publications

References 8 publications

Modeling Energy Demand—A Systematic Literature Review

Modeling Energy Demand—A Systematic Literature Review

Techno-economic optimization of configuration and capacity of a polygeneration system for the energy demands of a public swimming pool building

Investigation of the Energy Recovery Potentials in Ventilation Systems in Different Climates

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