Exploratory data analysis of the electrical energy demand in the time domain in Greece

Tyralis, Hristos; Karakatsanis, Georgios; Tzouka, Katerina; Mamassis, Nikοs

doi:10.1016/j.energy.2017.06.074

Cited by 13 publications

(7 citation statements)

References 69 publications

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“…The statistical method aimed to establish the relationship between the meteorological factors and the total energy consumption of a single building [14,15], energy consumption that has overcome the social and economic impacts by the de-trending method [16][17][18], or the components of energy consumption caused by seasonal changes obtained through multiplicative decomposition [19] for a region or city (Figure 1); or build the relationship between the total energy consumption [20,21], energy consumption per capita [6,22], energy consumption per household [6], or energy consumption per unit of gross domestic product (GDP) [23] of a city or region, and meteorological, social or economic factors. These meteorological factors can be classified as air temperature driven type, non-air temperature driven type, and compound driven types.…”

Section: Methodsmentioning

confidence: 99%

“…These meteorological factors can be classified as air temperature driven type, non-air temperature driven type, and compound driven types. The air temperature driven factors include hourly temperature [14], daily maximum temperature [14,16], minimum temperature [14], and mean temperature [14,18,20,23], monthly mean temperature [6], and degree days [14,17,24], degree-hours [25], degree-minutes [24], number of days during the heating period [22], power function of degree days [22] calculated based on temperature, etc. Non-air temperature driven factors mainly include the total solar radiation [24], average relative humidity [6,24], total precipitation [16,24], days of precipitation [6], average wind speed [6,24], average atmospheric pressure [24], and so on.…”

Section: Methodsmentioning

confidence: 99%

“…Res. Public Health 2020, 17, x FOR PEER REVIEW 3 of 17 product (GDP) [23] of a city or region, and meteorological, social or economic factors. These meteorological factors can be classified as air temperature driven type, non-air temperature driven type, and compound driven types.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

Wang

et al. 2020

IJERPH

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The world is faced with significant climate change, rapid urbanization, massive energy consumption, and tremendous pressure to reduce greenhouse gases. Building heating and cooling is one primary source of energy consumption and anthropogenic carbon dioxide emissions. First, this review presents previous studies that estimate the specific amount of climate change impact on building heating and cooling energy consumption, using the statistical method, physical model method, comprehensive assessment model method, and the combination method of statistical and physical model methods. Then, because the heating and cooling degree days indices can simply and reliably indicate the effects of climate on building heating and cooling energy consumption, previous studies were reviewed from the aspects of heating and cooling degree days indices, regional spatial-temporal variations in degree days and related indices, influencing factors of the spatial distributions of degree days, and the impacts of urbanization on degree days. Finally, several potential key issues or research directions were presented according to the research gaps or fields that need to be studied further in the future, such as developing methods to simply and accurately estimate the specified amounts of climate change impact on building cooling and heating energy consumption; using more effective methods to analyze the daytime, nighttime, and all-day spatial-temporal changes in different seasons in the past and future under various environment contexts by considering not only the air temperature but also the relative humidity, solar radiation, population, etc., and further exploring the corresponding more kinds of driving forces, including the various remotely sensed indices, albedo, nighttime light intensity, etc.; estimating the daytime, nighttime, and all-day impacts of urbanization on heating degree days (HDDs), cooling degree days (CDDs), and their sum (HDDs + CDDs) for vast cities in different environmental contexts at the station site, city, regional and global scales; producing and sharing of the related datasets; and analyzing the subsequent effects induced by climate change on the energy consumption for building heating and cooling, etc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

Wang

et al. 2020

IJERPH

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“…Another study by Baker and Rylatt (2008) tried to improve the prediction of energy demand by analyzing annual energy consumption data [12]. Meanwhile, Tyralis et al used EDA to understand the time series electrical energy demand data and building a forecasting model [13].…”

Section: Data Analysis Proceduresmentioning

confidence: 99%

Energy Consumption Data Analysis: Indonesia Perspective

Prastyabudi¹,

Hafidz²

2020

COMPLETE

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Energy consumption has been increasing affected by many factors such as population growth, energy price, etc. This paper utilizes data analysis to examine energy consumption data and its correlation the GDP growth, particularly in case of Indonesia. The data observed in this study comprises a record from 1961-2018. An exploratory data analysis was employed to comprehend the features of the data, while beforehand a pre-processing and testing were conducted to prepare the data. Data visualization was built then a Spearman correlation rank was also performed to see the association among variables. The result shows that the energy consumption is dominated by fossil generated energy i.e. oil, coal, gas. Meanwhile, the renewable energy was started to use since early 2000 with a huge leap of consumption on solar energy. The result of correlation ranking indicates that the energy consumption and the GPD growth has no significant association as the rank value is negative and the p-value is greater than the chosen significant level. Thus, we can conclude that the higher energy consumption does not directly affect the increase in GDP.

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“…This uncertainty strongly affects the management and operation of energy systems as well as the cost and sustainability of investments in the energy sector. On the other hand, energy demand is directly correlated to financial, environmental and societal factors (Tyralis et al, 2017b). Therefore, satisfying energy demand, or else achieving energy security, in a reliable and sustainable manner is a matter of great societal, financial and environmental importance, and it is considered one of the main technological goals of our era.…”

Section: Introductionmentioning

confidence: 99%

A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources

et al. 2018

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Abstract. The ever-increasing energy demand has led to overexploitation of fossil fuels deposits, while renewables offer a viable alternative. Since renewable energy resources derive from phenomena related to either atmospheric or geophysical processes, unpredictability is inherent to renewable energy systems. An innovative and simple stochastic tool, the climacogram, was chosen to explore the degree of unpredictability. By applying the climacogram across the related timeseries and spatial-series it was feasible to identify the degree of unpredictability in each process through the Hurst parameter, an index that quantifies the level of uncertainty. All examined processes display a Hurst parameter larger than 0.5, indicating increased uncertainty on the long term. This implies that only through stochastic analysis may renewable energy resources be reliably manageable and cost efficient. In this context, a pilot application of a hybrid renewable energy system in the Greek island of Astypalaia is discussed, for which we show how the uncertainty (in terms of variability) of the input hydrometeorological processes alters the uncertainty of the output energy values.

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Exploratory data analysis of the electrical energy demand in the time domain in Greece

Cited by 13 publications

References 69 publications

A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

Energy Consumption Data Analysis: Indonesia Perspective

A preliminary stochastic analysis of the uncertainty of natural processes related to renewable energy resources

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