Analysis and Forecasting of Electricity Usage in Industrial Water
Supply Firms: A Case Study of NMMC Water Supply Company
Using Polynomial Regression Method
Nurbek Kurbonov
1
, Amanklich Amanklichev
1
1
Tashkent State Technical University, University Str. 2, Tashkent 100095, Uzbekistan
nurbek.kurbonov.96, amanqilichevatajan)@gmail.com
https://doi.org/10.5281/zenodo.10470984
Keywords:
Quantitative Analysis, Gaussian Distribution, Polynomial Regression, Correlation Analysis, Energy
Consumption Forecasting, NMMC Water Supply Company, Thermal Insulation Improvement, Pump Station
Maintenance, Electrical Supply Scheme, CE308 Type Meter, Comparative Energy Consumption, Air
Temperature Impact, Water Delivery Amount, River Water Level Changes, Industrial Energy Consumption,
Energy Audit Methodology, Technical Water Processing, Gaussian Law Reliability Check, Comparative
Consumption Standards, Organizational and Technical Measures, Energy Management Systems (EMS), Air
Temperature Mitigation, Airtightness Improvement, Operational Demand Analysis, Environmental
Sustainability Goals, Energy Efficiency Enhancement, Cost Savings Strategies, Scalable Energy Management
Model, Operational Normative Revision, Long-term Benefits Analysis.
Abstract:
In this study, we examined the methodology for analyzing the electrical energy consumption of the Navoiy
Mining Metallurgical Combine's Water Supply Company (hereafter referred to as the NMMC Technical
Water Supply Company) and developed theoretical proposals to enhance energy efficiency. It is important to
note that the proposed theoretical solutions for improving electrical energy efficiency are applicable not only
to the NMMC Technical Water Supply Company but also to other industrial enterprises. This research
involved a thorough analysis of energy consumption indicators, with a primary focus on identifying the main
factors affecting electricity usage. The study highlights the identification and mitigation of key factors
influencing electricity consumption, thereby demonstrating potential reductions in electrical energy wastage
for the enterprise, and economically benefiting the global electricity system. Furthermore, the research
explores the implementation of Energy Management Systems as a strategic approach for monitoring,
analyzing, and optimizing energy consumption. Recommendations for improving air tightness to mitigate
temperature effects and the importance of continuous analysis and revision of normative indicators are
emphasized. Additionally, the entire technological process of the NMMC Technical Water Supply Company
was studied, pinpointing areas of high electrical energy consumption. Primary data on electricity consumption
at these points were verified for reliability using Gaussian distribution, ensuring the accuracy of all values
used. The study also presents forecasts of the company's electrical energy consumption in the forthcoming
months.
1 INTRODUCTION
Energy consumption in industrial enterprises is a
topic of increasing importance due to its economic
and environmental implications [1]. Many industrial
enterprises, such as the Navoiy Mining and
Metallurgy Combinat (NMMC), are significant
consumers of not just electrical energy but also
technical water, contributing to their overall energy
footprint [7]. Technical water, used in various
industrial applications like cooling and processing,
forms a crucial aspect of industrial operations, often
overlooked in conventional energy studies [3].
The NMMC water supply company, responsible for
supplying technical water, illustrates this intersection
of water and energy usage. Understanding its energy
consumption patterns is vital for devising energy
efficiency strategies [4,5,6]. This study aims to
analyze the energy consumption at NMMC with a
focus on identifying key factors influencing its
electricity usage, against the backdrop of a growing
emphasis on sustainable industrial practices [8,9].
By examining the NMMC case, this research
contributes to the broader discourse on energy
efficiency in industrial settings, where energy
consumption accounts for a significant portion of
operational costs and environmental impacts [10,11].
Thus, the findings of this study are expected to
provide valuable insights not only for NMMC but
also for other industrial enterprises facing similar
challenges.
2 Methods
The article utilises quantitative analysis,
Gaussian distribution, polynomial regression,
and correlation analysis.
3 Results
The
primary
research
involved
a
comprehensive analysis of the enterprise, leading
to the following findings:
1.
Future expected values of comparative
energy consumption were determined using
fourth, fifth, and sixth-degree polynomial
regression, with the fourth-degree model being
most accurate (MAPE=1%) and thus used for
forecasting.
2.
The forecast indicates a significant
increase in comparative electricity consumption in
the coming months.
3.
Analysis showed the need for improved
thermal insulation and maintenance of the building
housing the pumps, especially in response to sharp
temperature
drops.
Implementing
these
organisational and technical measures could
achieve standard energy consumption levels.
3 Discussion
1.
The electrical supply scheme of the
NMMC water supply company was studied. The
scheme, depicted in Figure 1, operates as follows:
initially, 220 kV from the Navoiy TPP is stepped
down to 6 kV in three stages at the NMMC
220/35/6 substation and then further reduced to 0.4
kV through small transformers in the plant for
electrical equipment supply.
For automatic accounting of electrical energy
consumption in this enterprise, a CE308 type
meter rated 5[10]A 3*57.7/100v, 50 Hz has been
installed based on technological calculations.
2. The enterprise has 10 pump stations, with
typically 2 pumps operating continuously. The
remaining pumps are activated as needed. While
the calculated electrical consumption for two
pumps is 24 MWh, the metered value is 20.256
MWh.
3. It was determined that the comparative
electrical energy consumption of the NMMC
water supply company is on average 30% higher
than the set standard. Correlation analysis of
factors affecting comparative electrical energy
consumption revealed that changes in air
temperature account for 71%, the amount of water
delivered 95%, and changes in river water level
9%.
4. The data for the analysis, based on the law
of Gaussian distribution, was deemed reliable, and
it was found to conform to the Gaussian law.
By 2022, industrial enterprises accounted for
45-50% of energy consumption. Therefore,
developing
and
rapidly
implementing
organizational and technical measures based on
simplified complex analysis methods for all types
of industrial enterprises is of great importance.
In this context, a method for primary rapid
inspection
and
analysis, followed
by
the
implementation of
primary
organizational and
technical
measures,
was
developed
using
the NMMC water
supply company as
a case study. Based
on
this,
a
methodology
for
conducting
a
small-scale energy audit using the most cost-
effective and efficient data collection and
calculation methods was developed (Figure 1).
The sequence of conducting a small-scale
energy audit as depicted in Figure 2 involves the
following steps:
A) Initially, the technological process of the
enterprise is studied;
Figure 1. Methodology for
Conducting a Small-Scale
Energy Audit.
The NMMC water supply company is
responsible for preparing and delivering technical
water to the relevant enterprises of NMMC. The
company accepts water from the Amu Darya using
natural flow into a 400 m³ pool, then pumps the
received water to the next four pools. In these
pools, specific reagents are added to the water,
transforming it into technical water, which is then
delivered to NMMC under high pressure (Figure
2).
1. Water movement; 2. Natural water receiving
pool; 3. Water intake pump; 4. Water pipeline; 5.
Water preparation pool; 6. Water transfer pump; 7.
Water transmission pipeline.
B) As a result of the studies, primary data
relevant to the enterprise is collected (Table 1);
Table 1.
Primary Data
Month
EE
[kVt·soat]
T
[K]
Water
level
[sm]
P [
𝒎
𝟑
]
𝒅
𝒇
April.2022y
6918660
293
514
8710967
0,8
May.2022y
7357680
295
580
8791007
0,8
June.2022y
7571700
301
590
8797225
0,8
July.2022y
7787160
302
619
8876330
0,8
August.2022y
8674020
298
665
8995214
0,8
September.2022y
7837500
295
601
8895619
0,8
October.2022y
7379820
287
519
8801197
0,8
November.2022y
7243740
281
549
8799527
0,8
December.2022y
6802020
272
635
8602413
0,8
January.2023y
6698900
270
676
8598941
0,8
February.2023y
6705180
278
682
8579563
0,8
March.2023y
6867540
289
551
8664566
0,8
From
this
table, we can
see that during
the phase of
receiving
primary
data,
established
standard
indicators (d
0
)
and initial 12-
month
electrical
energy
consumption
(EE),
air
temperature
(T), river water
level (sm), and
the amount of water transferred (P) are collected.
C) Checking the reliability of data collected for
the study;
It is essential to verify the reliability of the primary
data collected before using them in subsequent
stages. The reliability check is carried out based on
the Gaussian distribution law. According to the
law of Gaussian distribution, the average deviation
in the data collected based on certain processes
should be exponential. Analyses were conducted
on this basis [12,13]. This analysis was performed
using a Gaussian distribution analysis software.
Here, the average value of the incoming data µ was
determined, and the formula of the Gaussian law
dependent on µ was derived (Table 2).
µ =
1
N
∑
𝑃
𝑖
𝑖
𝑖=1
(1)
y(µ) =
1
µ√2𝜋
𝑒
−
Δ𝑥
𝑖
2
2µ
2
The results obtained through this Gaussian law
function can be visualized in the following graph
(Graph 1).
The values following the Gaussian distribution
law indicate that we can utilize primary data to
Figure 3. Process of Technical Water Transfer.
Figure 3. Gaussian Distribution.Water
Transfer.
Graph 3. Fourth-Degree Model of Polynomial
Regression.
y = 5E-05x
4
- 0,0008x
3
+ 0,0014x
2
+ 0,0221x
+ 0,7776
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0
5
10
15
develop a model in this case. This operation was
determined using the polynomial regression
method of univariate forecasting. With this
method, a fourth-degree polynomial model was
developed from the initial data: y = 5E-05x⁴ -
0.0008x³ + 0.0014x² + 0.0221x + 0.7776 (Graph
3). The error of this model was less than 5%,
making it viable for forecasting purposes. If the
error had been greater than 5%, it would have been
necessary to use other degrees in the model
development.
Based on this table, a correlation analysis was
conducted (Table 5). The correlation analysis indicates
the degree of impact of various factors on the electrical
energy consumption of the enterprise. Specifically, it
shows:
- k < 30% indicates a weak impact;
- 33% < k < 67% indicates a moderate impact;
- k > 67% indicates a high impact.
Table 5.
Correlation analysis
EE
P
T
Water
level
EE
1
P
0,96
1
T
0,72
0,78
1
Water level
0,090
-0,15
-0,30
1
In this table, the results are differentiated by
various colors, each representing the degree of
impact of influencing factors. These are
categorized as:
Weak;
Moderate;
High impact.
From this correlation analysis, it is observed
that the amount of water transferred and air
temperature have a high impact on the electrical
energy consumption of the enterprise. Reducing or
increasing the amount of water transferred is not a
feasible option, as it would significantly deviate
from the established comparative electrical energy
consumption standards in the enterprise [14,15].
G) Developing organizational measures based
on factors with a high impact on comparative
electrical energy consumption;
Here, measures can be taken to reduce the
impact of air temperature on electrical energy
consumption. For instance, improving the
airtightness of the building where the working
motors are installed can achieve this. This will
reduce the impact of external temperatures on
these motors.
As NMMC expands its operations, the
demand
for
technical
water
increases,
consequently increasing its electrical energy
consumption.
Continuous
analysis
of
the
enterprise becomes increasingly important, and the
use of Energy Management Systems (EMS) is
appropriate.
Utilizing
Energy
Management
Systems provides significant benefits to the
enterprise, automating the formation of results and
conducting correlation and regression analyses
automatically.
H1) Determining the expected outcomes of
these measures;
I) Implementation of organisational and
technical measures;
H2) Assessing the effectiveness of the
implemented
organizational
and
technical
measures;
M) Revising normative indicators;
T) After a certain period (according to the
procedure, in the next quarter), repeating this
process.
Conclusion
The comprehensive analysis conducted at
the NMMC water supply company has provided
valuable insights into the factors influencing its
electrical energy consumption. Our study
highlighted the significant impact of the amount of
water transferred and air temperature on the
company's energy usage, underscoring the need
for targeted organizational and technical measures.
The application of polynomial regression
for forecasting and the use of correlation analysis
to identify critical influencing factors have proven
to be effective tools in understanding and
predicting energy consumption patterns. The
implementation of Energy Management Systems
emerged as a key recommendation, offering a
structured approach to continuously monitor,
analyze, and optimize energy usage.
Our findings suggest that improving the
airtightness of facilities housing critical equipment
can significantly mitigate the impact of external
temperature variations, leading to more efficient
energy use. Moreover, this study underscores the
importance of regular analysis and revision of
normative indicators to keep pace with changes in
operational
demands
and
environmental
conditions.
As NMMC continues to expand, our
approach provides a scalable and repeatable model
for energy analysis and management. The
expected outcomes of the measures we've
proposed are likely to enhance energy efficiency
and contribute to cost savings, while also aligning
with broader environmental and sustainability
goals. The continuous application of these
methods and the adoption of best practices in
energy management are expected to yield
significant long-term benefits for the company,
setting a precedent for other enterprises in similar
sectors.
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