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Energy usage for operation of the swimming facility vs. the outdoor temperature, both daily averaged.
Source publication
This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility’s energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple line...
Contexts in source publication
Context 1
... dataset used for training the regression analysis comprises approximately 350,000 observations. Figure 4 shows the collected data for the dependent variable and the total electric and thermal power consumption, plotted together with the outdoor dry-bulb temperature. The average power consumption for the whole dataset is approximately 16 kW and energy supply for the period is 93,000 kWh. ...
Context 2
... registered average diurnal dry-bulb temperature ranges from −11 • C to 20 • C. During this period, nearly 2000 swimmers used the facility, equally divided between adults and youngsters/children. Figure 4 reveals a seasonal trend, a minor dependency between the energy use and the outdoor temperature, with some spikes in energy use distributed over the period. By visual inspection, it seems that the outdoor temperature variable can explain some of the variations in energy use, but additional variables influenced the variation in daily total energy usage. ...
Context 3
... the energy performance, the swimming facility at Jøa was identified as having an energy performance indicator (EPI) of 44.8 kWh/visitor, calculated over the period of the investigated dataset presented in Figure 4. In comparison, Norwegian swimming facilities are associated with an average EPI for a typical year of approximately 26 kWh/visitor,and a median EPI of approximately 22 kWh/visitor, where the dispersion is reported to range from 10 to 80 kWh/visitor [51]. ...
Citations
... . La modélisation des bassins de piscine dans la littérature Bien qu'une partie de la littérature de la modélisation des piscines s'appuie sur des modèles de type « boites noires » reposant sur des outils comme les réseaux de neurones artificiels[75], ou des méthodes statistiques[76], une grande majorité de ces derniers se base sur une modélisation phénoménologique[55],[77],[78],[79],[80]. On trouve dans la littérature plusieurs types d'applications comme les bassins de centrales nucléaires[22] ou les bassins de natation[21], ceux-ci pouvant être occupés[24] ou inoccupés[23], intérieurs[5] ou extérieurs[2], enterrés[23] ou hors sol[25]. ...
... The data on taxpayers who pay the largest taxes to SIT accounts, provided by the SIT website [29], shows that SIT mainly groups taxpayers by the amount of taxes and the sectors of activity (industries). It can also be seen that it separately analyzes the amount of tax paid by taxpayers, whose legal form is a public institution. ...
... Examples of SIT-implemented projects include "Transparent Production", "Responsible Construction", "Lossy Company", "Show without shadows", "Wheels", "Housing without Tax", and "White Gowns", where attributes perform the segmentation of taxpayers (e.g., the nature of the activity) and their behaviour (predisposition to certain behaviour). During these projects [29], the tax administrator intentionally takes control actions in the identified risk segments, where some common behaviours of taxpayers prevail (inclined to fail or maliciously avoid tax obligations). ...
Tax authorities face the challenge of effectively identifying companies that avoid paying taxes, which is not unique to European Union countries. Limited resources often constrain tax administrators, who traditionally rely on time-consuming and labour-intensive tax audit tools. As a result of this established practice, governments are losing a lot of tax revenue. The main objective of this study is to increase the efficiency of the detection of tax evasion by applying data mining methods in the East European country Lithuania, which has a rapidly developing economy, by applying data mining methods concerning affluence-related impacts. The study develops various models for segmentation, risk assessment, behavioral templates, and tax crime detection. Results show that the data mining technique can effectively detect tax evasion and extract hidden knowledge that can be used to reduce revenue losses resulting from tax evasion. This study’s methods, software, and findings can assist decision-makers, experts, and scientists in developing countries in predicting tax fraud detection.
... Its applications can also be found in various problem domains, such as in education [2][3][4][5][6][7][8][9], software size estimation [10][11][12], economics-finance [13], customer behavior [14][15][16][17], water quality [18], swimming pool facilities [19], medical researches [20] [34], etc. Regression models can help decision-makers by providing information on the interrelationship between variables (attributes), causal relationship equations, and prediction planning for future data [21]. In the data science era, the multiple linear regression model (MLR) is one of the top 10 machine learning (ML) algorithms because it incorporates a statistical method used in predictive analysis. ...
The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modelling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.
