Analysis and future outlook of natural gas consumption in the Italian residential sector

Bianco, Vincenzo; Scarpa, Federico; Tagliafico, Luca A.

doi:10.1016/j.enconman.2014.07.081

Cited by 94 publications

(47 citation statements)

References 22 publications

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“…To this end, the EU has developed a program to increase energy efficiency by 20% until 2020. Various studies [1] confirm that this goal is achievable. Currently, the main direction of Russia's government policy are: conducting energy audits, developing energy efficiency standards for certain types of products, revising old standards and adopting new standards for existing and newly constructed buildings for various purposes, promoting energy efficiency principles [2].…”

Section: Introductionmentioning

confidence: 85%

Design without thermal bridges

Maltseva

Elokhov²,

Tkachuk

et al. 2018

MATEC Web Conf.

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Abstract. The main focus of the article is on the quality design and implementation of the thermal insulation of buildings. Shells of buildings consist not only of conventional structures (wall, roof, ceiling), but also of edges, corners, joints and places of disruption of the outer thermal insulation shell integrity (due to through passage of pipelines, ventilation ducts and etc.). In all these places, heat losses are generally increased in comparison with conventional surfaces. Some of them are easily taken into account by calculation, using a special method proposed by the authors. Other thermal bridges due to unfavorable structural details can be avoided by observing the rules and recommendations that are classified and discussed in detail in this article.

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Section: Introductionmentioning

confidence: 85%

Design without thermal bridges

Maltseva

Elokhov²,

Tkachuk

et al. 2018

MATEC Web Conf.

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“…The model is expressed as a linear logarithmic function and it assumes the form of a standard dynamic constant elasticity function of the consumption [21,26,27]. ln(C res , k ) = β 0 + β 1 ln(HDD, k ) + β 2 ln(P res,k ) + β 3 ln(GDP PC,k ) + β 4 ln(C res , k−1 ) + β 5 ln(P res , k−1 ), (1) where C res represents the domestic gas consumption in bcm (billion cubic meters), HDD are the annual average heating degree days in • C-days, P res is the average gas price for residential customers in €/GJ HHV (high heating value) and GDP PC represents the GDP per capita in € per inhabitant, β i are the regression coefficients and the subscript "k − 1" refers to the lag term (i.e., a time lag of one year in the present case).…”

Section: Methodsmentioning

confidence: 99%

“…As for natural gas price, a correlation between Bundesamt fur Wirtschaft und Aufurcontrolle (BAFA) gas price (i.e., gas prices published by the German Federal Office of Economics and Export Control) and oil price is studied and utilized, and taxation levels in line with the historical values as given in [21] is assumed.…”

Section: True Forecasting Phasementioning

confidence: 99%

“…To this aim, a case study is proposed, namely the long term forecasting of natural gas consumption in the Italian household sector, which can be successfully described by a simple evolutionary equation driven by a number of explanatory variables as shown in [21]. Data from this study are updated and the analysis is verified by the use of a Kalman filter which, to the best of the authors' knowledge, has never been applied so far to evaluate the long term consumption of natural gas.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector

Scarpa

Bianco

2017

Energies

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(1) Background: The present paper aims at estimating the quality of the forecasts obtained by using one equation models. In particular, the focus is on the effect that the explanatory variables have on the forecasted quantity. The analysis is performed on the long term natural gas consumption in the Italian residential sector, but the same methodology can be applied to other contexts; (2) Methods: Different ex ante knowledge scenarios are built by associating different levels of confidence to the same set of explanatory variables. Forecasting results, coming from a standard regression algorithm and confirmed by a Kalman filter, are analyzed by means of covariance matrix propagation to assess the quality of the provided estimates; (3) Results: The outcomes show that one-equation models are very sensitive to the quality of the explanatory variables, therefore their erroneous estimation may have a relevant detrimental effect on the predictive accuracy of the model; (4) Conclusions: The overall ex ante forecasting accuracy of an example of one equation model is assessed. It has emerged that long-term forecasts need particular attention when the covered time horizon spans over decades. The information contained in the present paper is of interest for energy planners, supply network managers and policy makers in order to support their decisions.

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“…Khan [5] studied the dynamics of natural gas consumption in Pakistan and predicted the natural gas demand from 2012 to 2020 via an ordinary least squares (OLS) model. Bianco et al [6,7] predicted natural gas consumption by using a linear regression model with logarithmic transformations and considered gross domestic product per capita (GDP per capita), gas prices, and heating degree days (HDD). Akpinar and Yumusak [8] used the Holt-Winters exponential smoothing method and the autoregressive integrated moving average (ARIMA) model to forecast natural gas demand.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO‐NNGBM Model

Wang

Luo

Liu

et al. 2017

Mathematical Problems in Engineering

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The accurate and reasonable prediction of natural gas consumption is significant for the government to formulate energy planning. To this end, we use the multiverse optimizer (MVO) algorithm to optimize the parameters of the Nash nonlinear grey Bernoulli model (NNGBM (1,1)) and propose a hybrid MVO-NNGBM model to predict the natural gas consumption in 30 regions of China. The results indicate that the prediction precision of the hybrid MVO-NNGBM model is better than that of other grey-based models. According to the forecast results, China's natural gas consumption will grow rapidly over the next five years and reach 354.1 billion cubic meters (bcm) by 2020. Moreover, the spatial distribution of natural gas consumption will shift from being supply oriented towards being demand driven and will be mainly concentrated in coastal and developed provinces.

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Analysis and future outlook of natural gas consumption in the Italian residential sector

Cited by 94 publications

References 22 publications

Design without thermal bridges

Design without thermal bridges

Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector

Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO‐NNGBM Model

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