Analysis of Data Handling and Business Intelligence in Modern Business

This report provides a summary of an article focusing on multi-objective optimization for energy-saving control in data centers. The study highlights the application of business intelligence and advanced technologies to improve efficiency and reduce energy consumption. The article discusses the use of the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the performance of air conditioning systems (ACS) within data centers, aiming to minimize power consumption while maintaining stable rack intake air temperatures. The report details the methodology, which includes the use of feedforward neural networks (FNN) for modeling, and presents the results of experiments conducted to evaluate the effectiveness of the proposed energy-saving control schemes. The findings indicate significant energy savings, particularly in winter and summer. The report also emphasizes the role of Power Usage Effectiveness (PUE) as a key metric for assessing data center energy efficiency and discusses the integration of fresh air cooling control to further enhance energy-saving performance. Overall, the report underscores the importance of business intelligence and advanced technologies in achieving sustainable and efficient business operations.

Data Handling and Business intelligence

Table of Contents
INTRODUCTION...........................................................................................................................2
MAIN BODY..................................................................................................................................2
Summary of an article..................................................................................................................2
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................6

INTRODUCTION
Business intelligence is a set of process and technologies which are actually used to
convert the raw data into some meaningful information that helps a business to improve its
profit. That is why, most of the company uses this advance technique in order to improve its
overall business operation in better manner. In the same way, current study will help to
determine the importance of advance technology in the companies and its advantages over a
business. The study is based upon an article i.e. Multi- objective optimization of Energy saving
control for Air Conditioning System in Data Center. Study will provides a summary on the
selected article by throw a light on importance of an advance technology.
MAIN BODY
Summary of an article
In the modern era of digitalization, every company uses advance techniques and based
upon Internet of Things, big data, cloud computing etc. Most of the company also uses advance
IT equipment as well as uninterruptible power system are also placed into data centers. That is
why, a delicate Air conditioner system (ACS) is also installed in data center so that it will help to
remove heat exhaust from every area. So that, it will help to maintain constant indoor
temperature and humidity. The biggest advantage of using this system is such that it will help to
save the energy cost and also consume 70 billion kWh i.e. 1.8% of the overall energy
consumption.
From last many years, most of the IT professionals are also working and making
technological efforts in order to provide sustainable efforts of development, and as a result, a big
portion of data center power consumption is due to ACS that is proposed by the engineers in
order to save energy (Safikhani and Loloee, 2020). Also, engineers further proposed their
guidelines so that it will help to operate the system in better manner. Such that firstly, Power
Usage Effectiveness (PUE) is proposed this is define as a ratio of total power consumption in a
data center of IT equipment. While on the other side, engineers also state that minimize PUE is a
greatest challenge for them because ACS is main target for PUE as it is high power consumption.
So finally with the efforts of scientist, engineer and IT specialist, the system is developed that

helps to minimize energy and can be used by top companies and as a result, it provide cold
temperature for that particular premises.
While on the other side, the problem is also stated under the article that is, ACS is
develop in order to make energy saving which is completely relies upon the AHU fans and the
output chilled water temperature from the chillers. So, when any part of AC is damaged then it
affects the entire system and even there is no proper solution to take out from this. That is why,
Multi- objectives Optimization Approach has been provided and implemented by NSGA- II to
ACS of the campus data center. So that the average energy saving ratio are 23.4% for winter and
19.6% for summer days. Not only this, NSGA-II also contributed their best in order to evaluate
the fitness value which is corresponding to every chromosomes and Feed forward Network
(FNN) that is based upon the recorded data collected from the field (Yao and Huang, 2019).
Further, the scientist and engineers put their efforts in order to manage the issue so that
they further develop the best variety of Air Condition system for their customers. That is why,
they used metric PUE [7 to 9] in order to measure data center energy efficiency which is also
adopted to show the energy saving effectiveness. On the other and, for Multi- objective
optimization, the most straight forward and easiest way to control the chiller power consumption
is used that directly affect the AHY outlet cold air temperature. For that different formulas are
also used that helps to minimize the issue and maintain the temperature of the system in positive
manner.
Along with this, experiments are also perform in order to check the entire system so that
further modifications are performed. Also, the ACS in a campus data center is further utilized for
the experiments and it is consist of two AHUs and two chillers (Zhu, Wang and Sun, 2020). So,
one AHU and one chiller are using for proposed multivariable objectives energy saving control
schemes and on the other side, another AHU and chiller is further left uncontrolled so that it
provides the base load. Further, the experiment of neutral networking modeling is also perform
so that it will help to verify the modeling capability of the proposed three FNN models in the
equations and also these models are testing before implementation. Along with this, the engineer
or scientist selected randomly a single day from a week in order to testing and also perform
action in better manner. That is why, the article also perform the comparison of RMSE if
estimated signals for training and testing results in which it is analyzed that testing are done so

that accurate data are proposed in order to manage the work and deliver the best products to the
companies.
On the other side, experiments of energy saving also shows that effectiveness and
efficiency of the proposed energy saving control scheme, the control scheme was applied on a
typical summer right. Further, it also takes about the 120 s in every sampling interval for an
energy saving controller. Also, the formulas are used that helps to determine the exact
performance. There are different figures used that clearly explain or calculated the approach by
Multi- objective optimization within a field. Further, the calculation also explain and measured
chilled water outlet temperature as well as chiller power consumption which is went down as a
control interval started and returned to normal values. In addition to this, the same experiment is
also perform in summer days to determine that the same answer. Thus, it is realized that energy
saving effects was verified by comparing a pair of days with as well as without the proposed
energy saving control schemes. The main reason for testing the results in winter and summer is
to examine whether there is any outlet difference between the results or not.
Actually it is analyzed that there is no variation in the results and that is why, it is clearly
indicated that whatever the temperature, output are same and there is no changes in the cold
outlet. The only difference between these days is the average energy saved on the particular days
such that in winter it is saved 4.02 kW/h and in summer 3.05 kW/h. Thus, it is also analyzed that
the saving ratio are fixed which is assumed by the scientist i.e. 23.4% and 19.6% in winter and
summer respectively.
From the article, it is analyzed that the Multi- objective optimizations approach by
NGSA-II which was proposed to optimize the energy saving effectiveness. Also, the
optimization approach actually minimizes the power consumption of both chillers and AHU only
when scientist put AHU outlet cold air temperature in a specified range. Further, there are
specifically designed in order to save the energy for future and this in turn assist to provide cold
air in summer while hot air in winter days. Further, the article assist to use the standardized
metric for the data centers that is actually includes PUE and RCI for the optimization. Further, in
addition to this, the current article also use fresh air cooling control which is integrate with the
proposed energy saving control scheme so that it directly help to improve the overall energy
saving performance. This clearly indicates that using advance technology within a premises and

use of best tool to manage the environment will help to create positive impact upon the overall
business and working environment.
CONCLUSION
By summing up above report it has been concluded that advance technologies and
business intelligence plays an important role in the success of the business. In the same way,
current study is also concluded that developing an advance technology based Air conditioner in a
business will help company and homes to save energy for the future. That is why, most of the
companies always prefer to use techniques that also assist to meet the define aim and smoothing
the business operations. Further, report also concluded that for introducing new Air conditioner,
scientist and engineers has been made many efforts and also uses variety of formulas to make
sure that it will work in proper manner and finally they got success. Thus, it clearly indicates that
advance technology plays a crucial role in the success of a company.

REFERENCES
Books and Journals
Safikhani, H. and Loloee, M., 2020. Multi-objective optimization of comfort-cost for cooling and
heating systems at the faculty of engineering of Arak University. Modares Mechanical
Engineering. 20(3). pp.529-535.
Yao, L. and Huang, J.H., 2019. Multi-Objective Optimization of Energy Saving Control for Air
Conditioning System in Data Center. Energies. 12(8). p.1474.
Zhu, L., Wang, B. and Sun, Y., 2020. Multi-objective optimization for energy consumption,
daylighting and thermal comfort performance of rural tourism buildings in north
China. Building and Environment, p.106841.