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Data Availability Statement

Data available on request from the author: The data that support the findings of this study are available from the corresponding author upon reasonable request.

1. Introduction

Our study is based on data regarding the electric energy consumption of a nonresidential consumer in Romania. The data that are part of the present study were collected during January-December 2016. The measurements were performed using specialized smart metering devices situated at the nonresidential consumers' locations and stored in databases dedicated to the analyzed field. The measurements sampling was carried out on an hourly basis over the entire period of the calendar year.

The authors' concerns regarding forecasting energy consumption and using the obtained results in reducing it, can be seen in the previous researches that they have done on residential households. In (Oprea, Pirjan, Carutasu, Petrosanu, Bara, Stanica, & Coculescu, 2018) a mixed neural network approach has been used in order to provide an accurate method for forecasting the residential electricity consumption in smart homes complexes, using data recorded by sensors. The developed method was validated and further compiled, the idea being to incorporate it in the IoT cloud solution that was proposed in (Stanica, Carutasu, Pirjan, & Coculescu, 2018). The solution here was to optimize the electricity consumption and costs of households, based on analyzing disparate data collected from sensors and home appliances in smart homes.

In Europe, non-residential buildings represent 25% of the total building stock and are considered to be more heterogenous and more complex than residential buildings (Droutsa, Balaras, Dascalaki, Kontoyiannidis, & Argiriou, 2018). Out of these, the retail and wholesale buildings represent the leading sector, with 28% of non-residential stock floor area. However, according to the same paper, the available data and the studies that track energy performance in non-residential buildings are more limited compared to those for households.

Nevertheless, the existing reviews show that the research community is making efforts in this direction. Miller, Nagy, and Schlueter (2018) have done a review of 100 publications that used unsupervised machine learning techniques in order to analyze the performance of non-residential buildings. Most of the publications being reviewed focused on energy performance. The conclusions show that clustering algorithms (particularly k-means clustering) and visual analytics are commonly used, but other procedures and techniques are worth exploring as well.

In a similar study (Ruparathna, Hewage, & Sadiq, 2016), a number of research articles focusing on increasing energy efficiency in commercial and institutional buildings were reviewed. The study included only articles published in well-reputed journals. Three main approaches in the literature were identified, concentrating on technical, organizational, and behavioral changes. As an outcome of the comprehensive review, the authors proposed a strategy map for improving buildings energy performance, stating that their findings could set the basis for developing national and organizational strategies in this direction.

Other studies focused on identifying the most performant techniques for modelling and forecasting the energy consumption. Tso and Yau (2007) made a comparison between three different techniques for predicting energy consumption: regression analysis, decision tree, neural networks. In order to choose the best one, the authors suggested the idea of developing a platform that implements different models and therefore can assess their prediction performances.

Another article on electrical consumption forecasting methods, authored by Daut et al. (2017), is focusing on both conventional and artificial intelligence methods, comparing the performance of both of them. The article concluded that a hybrid of the two forecasting techniques could lead to better results.

Covering the same topic, Zhao and Magoules (2012) evaluated different models for energy consumption prediction, including statistical, engineering, and artificial intelligence models, and at the same time, emphasized the difficulty of making such predictions, since there are many factors that can influence them and must be taken into consideration.

Perez-Chacon, Talavera-Llames, Martinez-Alvarez, and Troncoso (2016) analyzed a big time series of data collected from the electricity consumption of two university buildings over a period of three years. For establishing patterns, the authors used the distributed version of k-means clustering algorithm for Apache Spark, for which they also tested its computational performance.

For the residential sector, the prediction of energy consumption is modelled in Fumo and Biswas (2015), which used simple and multiple linear regression analysis on hourly and daily collected data from a household. Also this paper promotes the idea of developing a user-friendly software for modelling and forecasting the energy consumption.

Another research direction aims to identify the factors that influence the electric energy consumption. In Ma et al. (2017) the authors perform a case study on a number of public non-residential buildings in China, by analyzing their energy consumption patterns and the factors influencing it. Similarly, Gutierrez-Pedrero, Tarancon, del Rio, and Alcantara (2018) also focused on determining the main factors influencing electricity consumption of non-residential sector, their results showing that higher technological progress and higher electricity retail prices lead to a reduction of the consumption intensity.

By analyzing the existing body of knowledge, one can identify a necessity, a clear need for modeling the variation of non-residential electricity consumption covering various time intervals in order to identify specific consumption profiles. Therefore, the main objective of this analysis was to find the variation mode of the electric energy consumption for various time intervals as shown in Sections 2 and 3, in order to identify specific consumption profiles. Our research was aimed at identifying the main statistical sizes for modelling the collected data. For a more accurate analysis, collected data were stored in a table having the fields: month, day-number, hour-number, and energy-consumption-MWh.

The reminder of the paper is structured as follows: Section 1 presents the statistical and mathematical methods and techniques for analyzing data of electric energy consumption, Section 2 contains the processing and results, in Section 3 is presented the data analysis by grouping on intervals of variation, Section 4 contains the computer model for data analysis, followed by the Conclusions Section.

2. Statistical and mathematical methods and techniques for analysing data of electric energy consumption

Since the data in this study are linearly distributed at one-hour intervals over a calendar year, we have tracked their statistical behaviour in the case of grouping on equal intervals of variation. The statistical and mathematical methods and techniques applied in the present study allowed us to develop a specific computer model, in which we identified:

- The amplitude of variation of the general overall consumption (C) on an hourly basis during January-December 2016, using the equation (1):

A = [C.sub.max] - [C.sub.min]. (1)

The number of groups, using Sturges' formula (Sturges, 1926; Scott, 2009)

k = [1 + 3.322/lg n], (2)

where n, in this case, has the value of 24, i.e. the number of hours analyzed on a daily basis.

- The size of the grouping interval, denoted by h, which represents the ratio between the consumption amplitude and the identified k number of groups, was determined, the calculation formula being equation (3)

h = A/k. (3)

Based on the statistical and economic support for the repartition of the value intervals samples, we used rounded intervals in order to carry out the calculations. Under these conditions, we identified the size of the grouping interval as 91 MWh.

- Starting from the minimum value of the determined sum and the size of the identified grouping interval, we constructed the vectors of the minimum and maximum limits of the grouping intervals. Based on these vectors, the grouping of the data on the electric energy consumption was made, in order to build the statistical indicators specific to the analysis of the value series on intervals of variation. The vectors of the grouping intervals limits (L) are input variables in the mathematical-computer model presented in Section 4.

[L.sub.min] = [[C.sub.min],[C.sub.min]+h,..., [C.sub.min]+(k-1) * h), [L.sub.max] = [[C.sub.min]+h,[C.sub.min]+h,...,[C.sub.min]+(k-1) * h].

- The center of each analysed interval was identified as the simple arithmetic mean of the interval bounds, according to equation (4):

[c.sub.i] ([c.sub.imin] + [c.sub.imax])/2. (4)

- The absolute frequency of each group (n) was calculated; this is equal to the number of statistical units having the value of the characteristic greater than or equal to the lower limit of the interval and less than or equal to the upper limit.

Subsequently, based on the absolute frequencies, the ascending and descending cumulative absolute frequencies at each group level were identified. Similarly, ascending and descending cumulative relative frequencies could be determined. The absolute, relative, and cumulative frequencies represent the support that allows the identification of the overall behaviour of the distribution of values in collectivity, especially of the central tendency to normality of the frequency repartition.

Systematization of data on electric energy consumption in 11 equal intervals of variation, as well as the statistical and economic interpretation and construction of histograms (Scott, 1979) and curves of cumulative frequencies, are presented in the results section.

When applying the selection method, the most common situations are those in which the theoretical repartition law is normal N (m, [sigma]) (Purcaru, 1997). For selections from statistical populations with normal repartitions, the probability theory states the following results:

Theorem 1. If {[X.sub.1],[X.sub.2],..., [X.sub.n]} is a selection of volume n in a statistical population characterized by a random variable that follows a normal distribution N(m,[sigma]), then the selection mean has a normal repartition of mean m and standard deviation [[sigma]/[square root of n],i.e.:

[ber.X] = [[X.sub.1]+[X.sub.2]+...+[X.sub.n]/n] [member of] N (m,[[sigma]/[square root of n]). (5)

Theorem 2. If [X.sub.1]+[X.sub.2]+...+[X.sub.n] are normally distributed random independent variables, [X.sub.k] [member of] N([m.sub.k],[[sigma].sub.k]), k [member of] 1, n, and [[alpha].sub.1],[[alpha].sub.2],...,[[alpha].sub.n] [member of] R, then the random variable

Y = [[SIGMA].sup.n.sub.k=1] [[alpha].sub.k][X.sub.k] [member of] N ([[SIGMA].sup.n.sub.k=1] [[alpha].sub.k][m.sub.k], [square root of [[SIGMA].sup.n.sub.k=1] [[alpha].sup.2.sub.k][[sigma].sup.2.sub.k]]) (6)

In particular, if [[alpha].sub.1] = [[alpha].sub.2] = ... = [[alpha].sub.n] = [1/n], we have:

Y = [[[SIGMA].sup.n.sup.k=1][X.sub.k]1/n] [member of] N ([[SIGMA].sup.n.sup.k=1][m.sub.k]/n,[square root of [[SIGMA].sup.n.sup.k=1][[sigma].sup.2.sub.k]]) (7)

From the estimation theory (Popescu, 1993), we know that the selection mean [bar.X] = [[X.sub.1]+[X.sub.2]+...+[X.sub.n]/n] is a fixed, consistent and efficient estimator for the mean m of the general statistical population, and the dispersion of selection [S.sup.2] = [[[SIGMA].sup.n.sup.k=1][([X.sub.k-[bar.X]]).sup.2]/n] represents a sufficiently consistent estimator for the dispersion [[sigma].sup.2] of the general population (Popovici, 2015). In case of small volume selections, the dispersion [[sigma].sup.2] is evaluated with the corrected dispersion of selection, given by the formula [S.sup.2] = [[[SIGMA].sup.n.sup.k=1][([X.sub.k-[bar.X]]).sup.2]/n-1]

3. Processing and results

For reasons related to the rigor of the statistical analysis, as well as to facilitate the calculation process for limiting the field of error propagation (measurement, calculation, method), we used calculation approximations in certain data processing and analysis. When processing the data, we have used the following hardware configuration: the ASUS Rampage V Extreme motherboard, the central processing unit Intel i7-5960x with 32 GB DDR4 quad channel and the GeForce GTX 1080 TI NVIDIA graphics card. The software configuration that we have used consists in the Windows 10 Educational Version 1803 operating system. Starting from the initial data underlying the present study, and from the mathematical model in section 1, we have calculated in Table 1 statistical and mathematical indicators for data systematization.

Table 1 contains the main numerical characteristics that allow the statistical and mathematical systematization of the recorded values for the intervals of variation of electric energy consumption, number of hours frequency, ascending cumulative absolute frequencies, descending cumulative absolute frequencies etc.

The statistical results led to the histograms represented by the Figures 1 and 2.

Figure 1 highlights the fact that since there are two grouping intervals with null absolute frequency, then it is necessary to remake the systematization.

Based on the experience gained from the analysis of previous studies in the electric energy field, we have reduced the number of grouping intervals to avoid the excessive fragmentation of the processed statistical collectivity. Thus, by using 6 grouping intervals corresponding to an amplitude of h = 180, Table 2 resulted.

By analyzing the results in Table 2, one can observe that the possibilities of the occurrence of null absolute frequencies were eliminated.

Corresponding to the values calculated in Table 2, histograms for absolute frequencies, relative frequencies, and ascending cumulative relative frequencies are shown in Figures 3 and 4.

The charts of histograms (Feedman & Diaconis, 1981) and cumulative frequencies indicate that the distribution of hourly electric energy consumption within a full 24hour horizon has a normal tendency. Our research aimed, for the argumentation of the normality hypothesis of theoretical repartition, to apply a concordance test, by which we verified the possibility of concordance between the data provided on the experience and the hypothesis made on the form of the theoretical repartition law.

For the application of the concordance tests, the selection repartition function is determined in advance, based on the observed data, grouped by intervals and expressed using the relative frequencies and the cumulative relative frequencies. Subsequently, the selection repartition function is compared with the hypothetical theoretical repartition of the general population (Poisson, binomial, exponential, normal repartition). The literature mentions several methodologies (Sivilevicius, Vislavicius & Braziunas, 2017; Teodorescu, 2015; Ahmad, Ahmed, Vveinhardt & Streimikiene, 2016) for the implementation of these studies: Pearson's [chi square] test, Kolmogorov-Smirnov's test.

In the case of normal repartition, Kolmogorov is one of the most used tests of concordance. According to this test, the selection repartition function of the observed data noted as [F*.sub.n](x) is compared to the hypothetical theoretical repartition of the general population noted as [F.sub.0] (x):

- if max|[F.sub.0](x)--[F*.sub.n](x)| <[[[lambda].sub. [alpha]]/[square root of n]]=, then there is concordance between [F*.sub.n](x) and [F.sub.0](x) and the hypothesis [H.sub.0]: F(x) = [F.sub.0](x) is accepted;

- if max |[F.sub.0](x)--[F*.sub.n](x)| [greater than or equal to] [[[lambda].sub. [alpha]]/[square root of n]]=, then there is no concordance between [F*.sub.n](x) and [F.sub.0](x) and the hypothesis [H.sub.0] is rejected,

where, to the given significance threshold a it corresponds, by the formula K([[lambda].sub. [alpha]]) = 1--[alpha], a value of [[lambda].sub. [alpha]], such that, for a given n volume of the selection, we identify the value [[lambda].sub. [alpha]] (Popescu, 1993).

Starting from the observations regarding the annual electric energy consumption on an hourly basis, grouped on intervals of variation and expressed by means of the relative frequencies and ascending relative cumulative frequencies, we checked the normality hypothesis of the repartition of the observed values.

The concordance hypothesis was created with the following formula:

[H.sub.0]: F(x) = [F.sub.0](x,m,[[sigma].sup.2]),

where [F.sup.0] is the normal repartition function of parameters m and [[sigma].sup.2], which are unknown, but estimated by:

- the selection mean [bar.X] = [[X.sub.1]+[X.sub.2]+...+[X.sub.n]/n],


- the dispersion of selection [S.sup.2] = [[[SIGMA].sup.n.sup.k=1][([X.sub.k-[bar.X]]).sup.2]/n-1]

We calculated the differences [F.sub.0](x)--[F*.sub.n](x) in Table 3 where: X successively takes the values of the right bounds of the intervals of variation.

As can be seen in Table 3, in column 4 we calculated the relative frequencies corresponding to each interval, and in column 5 the cumulative relative frequencies, i.e. the values of the repartition function of the selection [F*.sub.n](x). For calculating the values of the theoretical repartition function [F.sub.0](x) in column 8, we calculated the reduced standardised selection values (column 6) and the corresponding values of the Laplace function (column 7).

To test the [H.sub.0] concordance hypothesis, in column 9 we calculated the differences [F.sub.0](x)--[F*.sub.n](x) from which we obtained max|[F.sub.0](x)--[F*.sub.n](x)| = 0.17683.

Considering the significance threshold [alpha] = 0.005, we correspondingly found [[lambda].sub. [alpha]] = 1.358, resulting that [[[lambda].sub. [alpha]]/[square root of n]]= 0.2772.

Since max|[F.sub.0](x)--[F*.sub.n](x)| = 0.17683 < 0.2772, then the repartition normality hypothesis in Table 3 is accepted.

Therefore, we can assume that the evolution of the annual electric energy consumption has a normal repartition, with the parameters m = 1471.942625 and [sigma] = 385.7714135. This allowed us to use the theoretical normal repartition constructed beforehand, in order to evaluate the probability of the electric energy consumption, for any real value of it between the minimum and maximum limits of the possible field of variation.

The adjustment of the observation data based on this repartition has led to the results in Table 4 and the histogram in Figure 5.

4. Data analysis by grouping on intervals of variation

The results of the analysis and the grouping of data on intervals of variation are presented in Table 5 and were based on the estimation of the parameters (mean and dispersion) of the theoretical normal repartitions that approximate the selection repartitions. Within the intervals of variation ([I.sub.1].. [I.sub.6]) obtained by the data analysis in Table 2, we calculated the selection mean and the dispersion for each set of selection data.

As a result of researching various methods for approximation of data repartition, we identified that the adjustment of primary data by estimated normal repartition provides the ideal model applied to the hourly electric energy consumption for the January-December time series, as it can be seen in Table 6 for the interval of variation [I.sub.1] = [887.02-1067.02], Table 7 for intervals [I.sub.2] = [1067.02-1247.02], [I.sub.3] = [1247.02-1427.02], and [I.sub.4] = [1427.02-1607. 02]; Table 8 for [I.sub.5] = [1607.02-1787.02] and Table 9 for the interval of variation [I.sub.6] = [1787.02-1967.02].

The normal distribution (Kosareva & Krylovas, 2011) of the hourly electric energy consumption values is confirmed by the graph of the repartition of the adjusted values for Figure 7.

Figures 7 a-d show that the processing of initial data led to obtaining adjusted values whose distribution corresponds to the normal distribution. This demonstrates the possibility of forecasting the electricity consumption based on the estimated normal distribution.

5. Computer model for analysis

In order to make this study more efficient, we propose the construction of a software system that corresponds to the mathematical support presented in section 1. The functional diagram of the proposed system is presented in Figure 8.

For the modelling and validation module, the analysis and processing techniques are specific to each methodology. The principle of their application is common, and it seeks to specify the form of the theoretical repartition function both in the case when its parameters are known, but also when the parameters are estimated based on the research data.

The decisional situation is characterized by the degree of certainty of the consequences of each formulated alternative. For the choice of decisions, the Electre method was used in situations where there are several possible variants Vi (i=1,m) to reach a goal, the evaluation is based on Cj (j=1,n) criteria, based on which the possible variants are compared two by two.

Various software for data analysis exist, but the data included in the present study required specific processing, which led to the need to develop our own software application for implementing the mathematical model used in the analysis.

The software application has been developed using a modular approach. Therefore, the "Data collection" module offers the possibility to collect, store, process, and archive data in a database. The "Modelling" module provides functionalities to model data, obtain decisions based on the modeled data, and achieve forecasts of the electricity consumption for non-residential consumers. The "Statistical indicators" module provides the possibility to compute the statistical indicators, to build and process grouping intervals. The "Mathematics of data" module implements the statistical tests and methods for verifying and validating the statistical repartitions, used for approximating the repartitions of the experimental data. It offers the possibility to use mathematical techniques in order to model data, to test them based on the Kolmogorov test, and to build assignment functions.

6. Conclusions

Assuming that the analyzed phenomenon keeps its trend of evolution, the estimated normal repartition can be used to forecast the electric energy consumption. The data sampling allowed a detailed analysis that reflects as accurately as possible the actual process studied for the analyzed consumer data. As a result of the research of various methods for approximating the data repartition, we have identified that the adjustment of the primary data with the estimated normal repartition provides the ideal model for hourly electric energy consumption for the January-December time series. Using the normal theoretical repartition obtained, we can assess the likelihood that the electric energy consumption varies continuously in the analyzed intervals. Furthermore, in order to model the data, it is necessary to use a dedicated computer system that contains specific analysis functions, which continuously adapt for new input data as well.


This work was funded by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS (National Research Council) / CCCDI (Advisory Council for Research, Development and Innovation) -UEFISCDI (Executive Agency for Higher Education, Research, Development and Innovation Funding), project number PN-III-P2-2.1-BG-2016-0286 "Informatics solutions for electricity consumption analysis and optimization in smart grids" and contract no. 77BG/2016, within the National Plan for Research, Development and Innovation for the period 2015-2020 (PNCDI III).


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George CARUTASU (1)

Alexandru PIRJAN (2)

Cristina COCULESCU (3)

Justina Lavinia STANICA (4*)

Mironela PIRNAU (5)

(1) Prof. PhD. habil., Faculty of Computer Science for Business Management, Romanian-American University, Bucharest, Romania,

(2) Prof. PhD. habil., Faculty of Computer Science for Business Management, Romanian-American University, Bucharest, Romania,

(3) Assoc. prof. PhD., Faculty of Computer Science for Business Management, Romanian-American University, Bucharest, Romania,

(4*) corresponding author, Lecturer PhD., Faculty of Computer Science for Business Management, Romanian-American University, Bucharest, Romania,

(5) Assoc. prof. PhD., Faculty of Informatics, Titu Maiorescu University, Faculty of Computer Science for Business Management, Romanian-American University, Bucharest, Romania,
Table 1. Statistical and mathematical indicators for data

Intervals of     Value     Value     Number     Percenta   Center
variation of     of class  of class  of hours   ge         of
electric energy  1         2         frequenc              interval
consumption                          y

 887.02-978.02    887.02    978.02   5          0.21        932.52
 978.02-1069.02   978.02   1069.02   2          0.08       1023.52
1069.02-1160.02  1069.02   1160.02   0          0          1114.52
1160.02-1251.02  1160.02   1251.02   1          0.04       1205.52
1251.02-1342.02  1251.02   1342.02   1          0.04       1296.52
1342.02-1433.02  1342.02   1433.02   1          0.04       1387.52
1433.02-1524.02  1433.02   1524.02   1          0.04       1478.52
1524.02-1615.02  1524.02   1615.02   0          0          1569.52
1615.02-1706.02  1615.02   1706.02   2          0.08       1660.52
1706.02-1797.02  1706.02   1797.02   4          0.17       1751.52
1797.02-1888.02  1797.02   1888.02   7          0.29       1842.52

Intervals of      Ascending     Descending cumulative absolute
variation of      cumulative    frequencies
electric energy   absolute
consumption       frequencies

 887.02-978.02     5            24
 978.02-1069.02    7            19
1069.02-1160.02    7            17
1160.02-1251.02    8            17
1251.02-1342.02    9            16
1342.02-1433.02   10            15
1433.02-1524.02   11            14
1524.02-1615.02   11            13
1615.02-1706.02   13            13
1706.02-1797.02   17            11
1797.02-1888.02   24             7

Table 2. Statistical and mathematical indicators for amplitude h=180

Intervals of      Absolute      Relative       Ascending    Descending
variation of      frequencies   frequencies    cumulative   cumulative
electric energy   (number of    (percentage)   absolute     absolute
consumption       hours)                       frequencies  frequencies

 887.02-1067.02   6             0.25            6           24
1067.02-1247.02   2             0.08            8           18
1247.02-1427.02   2             0.08           10           16
1427.02-1607.02   1             0.04           11           14
1607.02-1787.02   6             0.25           17           13
1787.02-1967.02   7             0.29           24            7

Intervals of      Ascending cumulative relative frequencies
variation of
electric energy

 887.02-1067.02   0.25
1067.02-1247.02   0.33
1247.02-1427.02   0.42
1427.02-1607.02   0.46
1607.02-1787.02   0.71
1787.02-1967.02   1

Table 3. Calculation of the differences [F.sub.0](x) - [F*.sub.n](x)

Intervals of      Interval   Numbei      Relative      Ascending
variation of      right      hours       frequencies   cumulative
the electric      limit      frequency   ([N.sub.K])   relative
energy            (x)                                  frequency
consumption                                            ([F*.sub.n])
1                 2           3          4             5

 887.02-1067.02   1067.02     6          0.25          0.25
1067.02-1247.02   1247.02     2          0.08          0.33
1247.02-1427.02   1427.02     2          0.08          0.42
1427.02-1607.02   1607.02     1          0.04          0.46
1607.02-1787.02   1787.02     6          0.25          0.71
1787.02-1967.02   1967.02     7          0.29          1
Total                        24

Intervals of     Reduced        Laplace   Reduced       [F.sub.0](x)
variation of     standardise d  values    normal        -[F*.sub.n] ()x)
the electric     values         [PHI](z)  repartition
energy           (z =                     function
consumption      [x-[bar.x]/s]            [F.sub.0](x)
                                          + [PHI](z)
1                6              7         8             9

 887.02-1067.02  -1.05          -0.35314  0.14686       -0.10314
1067.02-1247.02  -0.58          -0.21904  0.28096       -0.04904
1247.02-1427.02  -0.12          -0.04776  0.45224        0.03224
1427.02-1607.02   0.35           0.13683  0.63683        0.17683
1607.02-1787.02   0.82           0.29389  0.79389        0.08389
1787.02-1967.02   1.28           0.39973  0.89973       -0.10027

Table 4. Adjusted values

Hour  Hourly annual  Hourly annual standardised  Normal standardised
      consumption    consumption                 distribution of the
      x              (x-m)/[sigma]               consumption N(0,1)

 1     981.487       -1.27136332                 0.101799713
 2     942.031       -1.373641505                0.084776502
 3     920.86        -1.428521155                0.076570955
 4     899.077       -1.484987236                0.068773603
 5     887.02        -1.516241496                0.064729149
 6     955.821       -1.337894948                0.090465342
 7    1286.612       -0.480415652                0.315465934
 8    1418.438       -0.138695152                0.444845524
 9    1671.148        0.516381899                0.697206147
10    1741.403        0.698497518                0.757566945
11    1779.658        0.797662461                0.787466803
12    1830.843        0.930344661                0.82390367
13    1859.225        1.003916728                0.842290623
14    1876.665        1.049124847                0.852939669
15    1883.546        1.066961834                0.857005465
16    1863.549        1.015125438                0.844976981
17    1837.069        0.946483752                0.828049047
18    1818.669        0.898787113                0.815616967
19    1764.055        0.757216229                0.775539836
20    1711.728        0.621573726                0.732888899
21    1636.821        0.427399152                0.665455688
22    1475.068        0.008101624                0.503232045
23    1218.669       -0.656538085                0.255738986
24    1067.161       -1.049278435                0.147024994

Table 5. Intervals of variation of energy consumption [I.sub.1] =

Interval                       hour  X selection mean   stdev

[I.sub.1] = [887.02-1067.02]    1     81.79058333        8.806464
                                2     78.50258333        8.862347
                                3     76.73833333        7.876533
                                4     74.92308333        7.823663
                                5     73.91833333        7.611099
                                6     79.65175           7.912416
[I.sub.2] = [1067.02-1247.02]  23    101.5558           17.4152642
                               24     88.93008          12.5056917
[I.sub.3] = [1247.02-1427.02]   7    107.2176667         9.460543232
                                8    118.2031667        11.19081416
[I.sub.4] = [1427.02-1607.02]  22    122.9223           18.3328563
[I.sub.5] = [1607.02-1787.02]   9    139.2623           15.09059277
                               10    145.1169           17.40209719
                               11    148.3048           19.02851054
                               19    147.0046           23.7693493
                               20    142.644            23.10819802
                               21    136.4018           21.54638284
                               12    152.5703           21.15601997
                               13    154.9354           22.50710768
                               14    156.3888           24.16318601
[I.sub.6] = [1787.02-1967.02]  15    156.9622           24.87144687
                               16    155.2958           25.09644122
                               17    153.0891           25.07614269
                               18    151.5558           25.21181051

Table 6. Intervals of variation of the electric energy consumption
[I.sub.1]= [887.02-1067.02]

                 [I.sub.1]= [887.02-1067.02]
Month   1       2       3       4       5       6

Jan     0.355   0.394   0.437   0.491   0.438   0.412
Feb     0.138   0.155   0.142   0.139   0.122   0.131
March   0.161   0.174   0.187   0.129   0.171   0.159
April   0.184   0.168   0.132   0.135   0.117   0.124
May     0.265   0.274   0.281   0.266   0.264   0.255
June    0.908   0.897   0.885   0.862   0.88    0.849
July    0.968   0.968   0.966   0.962   0.955   0.949
Aug     0.926   0.935   0.934   0.934   0.941   0.948
Sept    0.549   0.542   0.528   0.502   0.518   0.544
Oct     0.428   0.435   0.5     0.6     0.53    0.567
Nov     0.394   0.378   0.431   0.422   0.481   0.595
Dec     0.375   0.313   0.27    0.318   0.337   0.255

Table 7. Intervals of variation of the electric energy consumption
[I.sub.2], [I.sub.3] and [I.sub.4].

       [I.sub.2] = [1067.02-1247.02]   [I.sub.3] = [1247.02-1427.02]
       Hour                            Hour
Month  23      24                      7       8

Jan    0.301   0.304                   0.343   0.388
Feb    0.136   0.106                   0.104   0.112
March  0.175   0.122                   0.196   0.175
April  0.203   0.21                    0.146   0.129
May    0.259   0.244                   0.228   0.214
June   0.887   0.874                   0.827   0.831
July   0.962   0.953                   0.953   0.959
Aug    0.941   0.909                   0.926   0.931
Sept   0.643   0.577                   0.579   0.478
Oct    0.36    0.405                   0.606   0.59
Nov    0.325   0.341                   0.718   0.686
Dec    0.47    0.765                   0.216   0.316

        [I.sub.4] = [1427.02-1607.02]
Month   22

Jan     0.347835
Feb     0.226021
March   0.2908
April   0.287687
May     0.358772
June    0.18391
July    0.072961
Aug     0.115001
Sept    0.393751
Oct     0.383884
Nov     0.392983
Dec     0.308018

Table 8. Intervals of variation of the electric energy consumption

                    [I.sub.5] = [1607.02-1787.02]
Month   9       10      11      19      20      21

Jan     0.343   0.338   0.327   0.327   0.327   0.31
Feb     0.119   0.126   0.126   0.158   0.151   0.14
March   0.222   0.238   0.231   0.214   0.222   0.22
April   0.157   0.174   0.179   0.192   0.185   0.207
May     0.283   0.263   0.267   0.271   0.279   0.301
June    0.856   0.849   0.854   0.873   0.871   0.875
July    0.973   0.977   0.979   0.971   0.971   0.971
Aug     0.94    0.938   0.929   0.944   0.942   0.942
Sept    0.446   0.473   0.521   0.598   0.596   0.604
Oct     0.491   0.445   0.435   0.365   0.392   0.394
Nov     0.526   0.505   0.469   0.432   0.435   0.417
Dec     0.304   0.299   0.307   0.264   0.246   0.238

Table 9. Intervals of variation of the electric energy consumption

                        [I.sub.6] = [1787.02-1967.02]
Month   12      13      14      15      16      17      18

Jan     0.301   0.3     0.294   0.3     0.299   0.302   0.309
Feb     0.132   0.142   0.146   0.145   0.15    0.142   0.143
March   0.227   0.225   0.222   0.212   0.213   0.209   0.202
April   0.199   0.206   0.206   0.208   0.212   0.215   0.208
May     0.286   0.289   0.294   0.308   0.309   0.311   0.295
June    0.874   0.874   0.876   0.879   0.882   0.876   0.872
July    0.976   0.976   0.974   0.972   0.971   0.97    0.971
Aug     0.931   0.934   0.935   0.937   0.94    0.942   0.942
Sept    0.558   0.577   0.607   0.618   0.624   0.622   0.612
Oct     0.408   0.383   0.383   0.384   0.364   0.363   0.369
Nov     0.45    0.424   0.415   0.405   0.383   0.389   0.419
Dec     0.274   0.271   0.259   0.25    0.259   0.269   0.275
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Author:Carutasu, George; Pirjan, Alexandru; Coculescu, Cristina; Stanica, Justina Lavinia; Pirnau, Mironela
Publication:Journal of Information Systems & Operations Management
Geographic Code:4EXRO
Date:Dec 1, 2019

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