**2. A closer look at previous related works**

Introduction of renewable strength sources can enhance the diesel generators used in the base stations of all Nigerian carriers, in wind farms, the place the source of electricity is stochastic, the inefficiency of enhancing the percentage efficiency of the mills to raise the production capacity of the industries that fully rely on the generator for their each day manufacturing is addressed with the aid of strength efficiency upgrades of doubly fed induction generator machines, the use of adaptive manipulate approach [7]. In our verbal exchange network, the lengthen in sending data from the transmit point to the get hold of factor is a very massive problem, so the power provides desires to be reliable; the minimization in electricity supply in the United States has induced financial troubles to small-scale industries; one principal hassle attributed to this is inadequate planning mechanism that will forecast the required amount of electricity that will be needed to feed the complete population [8]. During the current period, the renewable energy source has dramatically extended both qualitative and quantitative enhancements with growing pressure to overcome environmental and financial crises; taking awareness of the reality that passing information or transfer of data from one factor to the other has ended up a chronic problem in our communication industry, energy efficiency techniques should be adopted such as laptop; learning this ought to properly improve the robustness of information network, the usage of adaptive modulation technique [9]. Recently, ways to improve energy efficiency have been delivered to improve demand-side management of strength distribution systems. Measures brought in the work of [10] place optimized genetic algorithm (OGA), which was used to improve epileptic electricity provided from the country-wide grid due to instability that has been a problem to strengthen consumers. This instability in strength furnished skilled in energy distribution network ought to be minimized with the aid of introducing optimized genetic algorithm (OGA). Recently, voltage adjustment primarily based on reinforcement gaining knowledge of and distribution evaluation is additionally gaining popularity; renewable energy penetration into the power mix in mild of the developing global demand for strength has elevated the distribution and power satisfaction considerably. Renewable strength systems (RESs) had been hastily developed due to ecological, social, economic, and environmental elements such as the extensively established photovoltaic structures (PVs) and wind turbine systems (WTs) [11]. In learning about [12], Nigeria electricity gadget is confronted with a sequence of technical challenges due to long, radial, weak, and aging transmission network; this paper introduces the idea of electricity conservation and related technologies, as properly as choices to help users achieve the benefits of electricity efficiency improvements. The work includes using computer learning to improve power first-class and decreasing strength consumption as in the case of the telecommunication base transceiver station (BTS).

Research has been carried out on strength effectivity enhancement methods for the motive of reduction in electricity consumption and environmental pollution from unburnt hydrocarbons. Today's standard wireless get admission to networks consumes more than 50% of the total power consumption of mobile communications networks, which excludes the energy fed on via the cell stations (user terminals) whose more than 50% of electricity consumption is without delay attributed

to the base station (BTS) equipment. However, a discount on the electricity consumption of cellular networks is of remarkable importance from within your budget (cost reduction), environmental (decreased CO2 emissions), and efficiency perspectives. Hence, each reduction in strength consumption and CO2 emission are key drivers for the future of the ICT industry. In the latest file by way of the International Telecommunications Union (ITU) and Alliance for Telecommunications Industry Solutions (ATIS), a quantity of energy environment-friendly practices and strategies for consideration by way of agencies seeking to gain larger efficiencies within their wi-fi networks have been outlined.

There are lively lookup works on energy consumption, reduction, and efficiency in wireless get entry to networks, but issues touching on to the implementation of desktop learning approach had not been utilized considerably and explicitly addressed. This paper investigates power consumption of base transceivers stations (BTS), schemes that may want to doubtlessly reduce the power consumption have been described, and the management of reusing the conserved energy barring compromising first-class of the carrier (QoS) of the community explored. The research additionally investigates the importance of deploying optimization methods on strength efficiency.

We know that base transceiver station (BTS) is a transceiver that acts as an interface between the mobile stations (MS) to the network. A BTS will have between 1 and 16 transceivers (TRX) depending on the geography and demand for the provider of an area. Each TRX represents one ARFCN (absolute radio-frequency channel number). However, relying on geography, carrier demand, and operator's network method and architecture, a BTS can also host up to two, three, or six sectors, or a cell might also be serviced using various BTSs with redundant sector coverage. Each area is protected by a quarter antenna, which is a directional antenna. **Figure 1** indicates the typical macro BTS we found today. A range of remarkable documentations of hooked-up research techniques and philosophy have been mentioned extensively. Unfortunately, little comparison and integration throughout studies exist. In this article, a frequent appreciation of computer mastering and sensible agents' research and its implementation was undertaken. This paper does not extensively discuss electricity effectivity technologies but is looking to utilize modern-day mathematical strategies to successfully minimize strength consumption to retailer price in the industries. A dialogue on the framework protecting the literature on AI and ML research is restricted to energy effectivity techniques. Rather, it attempts to supply a beginning factor for integrating understanding throughout research in this area and suggests paths for future research. It explores research in certain novel disciplines: environmental pollution, medicine, maintenance, manufacturing, etc. Further lookup is wished to lengthen the current boundary of know-how in computer learning and optimization approaches. Utilizing machine gaining knowledge of disciplines into the current AI frameworks should through greater light maximize the beneficial properties of this strategy. This paper provides precious thoughts and perspectives for undergoing research on AI and ML. The closing aim was once to comprehend a reduction in electricity consumption using power efficiency means. The work offers a basis for future implementation of intelligent agents and machine learning strategies to achieve power savings that would finally translate to costs savings.

#### **3. Materials and methods**

To get the desired results and achieve our purpose, there is a need to follow the stated objectives sequentially and observe the procedure.

*Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*

Characterize and determine the power consumption of the modules of the cell site under study; to characterize the cell site and determine its power consumption, the type of base station (BS), configuration model, transceiver, and power models were inspected. The cell site or base station or base transceiver station (BTS) is a microcell managed by IHS Towers West Africa Limited, with site No. IHS-EN-T4670—2G/3G/4G networks (Indoor/Outdoor Site), housing MTN Nig Ltd and Airtel Nig Ltd base station equipment at Mount Street by Idaw River Layout Awkunanaw, Enugu. The site control (hop) about 30 other MTN/Airtel base stations (Terminal and Fiber sites) within its coverage area, it handles transmission (TX) and reception (RX) of voice, data, and streaming services.

A period of 27 days was used to monitor and carry out the measurements. The days include the morning period (peak), afternoon (off-peak), and evening/night (main peak). The readings are shown in sub-section 3.2.

#### **3.1 Obtaining the data from the company under study and determining the power consumption of the cell modules to be optimized**

The method used for data collection was a time series method of measurement for 27 days at the cell site under study.

In each day, measurements were taken at a period of 2 h, with an interval of every 15 min for 8 times. In the end, an average for the eight intervals was taken for each day for all equipment.

For instance, on Day 1, the 2G BTS Airtel with the current of 25 Amps as the average of eight intervals for every 15 min in the 2 h has the following current readings of 25.4 A; 24.6 A; 25.8 A; 24.3 A; 25.6 A; 24.4 A; 24.7 A; and 25.4 A for the intervals. The average is

$$Average = \frac{25.4 + 24.6 + 25.8 + 24.3 + 25.6 + 24.4 + 24.7 + 25.4}{8}$$

$$= 25.03 \text{Amps} \cong 25 \text{Amps}$$

An example of the 2G BTS Airtel measurement process is shown in Table C1 in Appendix C. The measurement was first performed at the BTS equipment booth, which is the backbone of the cell site that houses the transmitter and receiver modules. BTS is also connected to equipment on the tower for broadcast and hopping activities with other cell sites linked to the tower via RF and microwave antennas. At the end of the measurements performed, a 27-day summary was calculated.

#### **3.2 To determine the module to go on sleep mode and its power requirement**

$$\text{Power consumed} = \text{P}\_{\text{Con}} = \text{V}\_{\text{Aver}} \times \text{I}\_{\text{T}} \text{ (Watt)} \tag{1}$$

where PCon is the power consumed in (Watt); VAver is the average voltage calculated from each day measurement (Volt); IT is the average total current consumed by the equipment in the cell site (Amps).

• Day 1 at 13:30–15:30 h on 3rd September 2019

Total current ¼ IT ¼ 109*:*8 Amps Average voltage ¼ VAver ¼ 52*:*5 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 109*:*8 � 52*:*5 ¼ 5764*:*50 Watts

• Day 2 at 11:00–13:00 h on 4th September 2019

Total current ¼ IT ¼ 102*:*4 Amps Average voltage ¼ VAver ¼ 50*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 102*:*4 � 50*:*7 ¼ 5191*:*68 Watts

• Day 3 at 15:00–17:00 h on 5th September 2019

Total current ¼ IT ¼ 110*:*8 Amps Average voltage ¼ VAver ¼ 52 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 110*:*8 � 52 ¼ 5761*:*60 Watts

• Day 4 at 10:00–12:00 h on 6th September 2019

Total current ¼ IT ¼ 94*:*9 Amps Average voltage ¼ VAver ¼ 52*:*8 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 94*:*9 � 52*:*8 ¼ 5010*:*72 Watts

• Day 5 at 14:00–16:00 h on 9th September 2019

Total current ¼ IT ¼ 105*:*6 Amps Average voltage ¼ VAver ¼ 51*:*3 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 105*:*6 � 51*:*3 ¼ 5417*:*28 Watts

• Day 6 at 10:30–12:30 h on 11th September 2019

Total current ¼ IT ¼ 98*:*5 Amps Average voltage ¼ VAver ¼ 52*:*3 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 98*:*5 � 52*:*3 ¼ 5151*:*55 Watts

• Day 7 at 16:00–18:00 h on 12th September 2019

Total current ¼ IT ¼ 107*:*9 Amps Average voltage ¼ VAver ¼ 52*:*9 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 107*:*9 � 52*:*9 ¼ 5707*:*91 Watts

• Day 8 at 12:00–14:00 h on 15th September 2019

Total current ¼ IT ¼ 97*:*5 Amps Average voltage ¼ VAver ¼ 52*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 97*:*5 � 52*:*7 ¼ 5138*:*25 Watts

*Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*

• Day 9 at 15:30–17:30 h on 17th September 2019

Total current ¼ IT ¼ 108*:*3 Amps Average voltage ¼ VAver ¼ 52*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 108*:*3 � 52*:*4 ¼ 5674*:*92 Watts

• Day 10 at 14:30–16:30 h on 19th September 2019

Total current ¼ IT ¼ 106*:*9 Amps Average voltage ¼ VAver ¼ 53*:*2 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 106*:*9 � 53*:*2 ¼ 5687*:*08 Watts

• Day 11 at 13:00–15:00 h on 20th September 2019

Total current ¼ IT ¼ 104*:*7 Amps Average voltage ¼ VAver ¼ 52*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 104*:*7 � 52*:*7 ¼ 5517*:*69 Watts

• Day 12 at 19:00–21:00 h on 23rd September 2019

Total current ¼ IT ¼ 160*:*8 Amps Average voltage ¼ VAver ¼ 53*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 160*:*8 � 53*:*7 ¼ 8634*:*96 Watts

• Day 13 at 18:00–20:00 h on 26th September 2019

Total current ¼ IT ¼ 151*:*1 Amps Average voltage ¼ VAver ¼ 53*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 151*:*1 � 53*:*7 ¼ 8114*:*07 Watts

• Day 14 at 20:00–22:00 h on 27th September 2019

Total current ¼ IT ¼ 144*:*3 Amps Average voltage ¼ VAver ¼ 52*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 141*:*3 � 52*:*4 ¼ 7404*:*12 Watts

• Day 15 at 18:30–20:30 h on 28th September 2019

Total current ¼ IT ¼ 151*:*7 Amps Average voltage ¼ VAver ¼ 53*:*3 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 151*:*7 � 53*:*3 ¼ 8085*:*61 Watts

• Day 16 at 19:30–21:30 h on 2nd October 2019

Total current ¼ IT ¼ 153 Amps Average voltage ¼ VAver ¼ 53*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 153 � 53*:*4 ¼ 8170*:*20 Watts

• Day 17 at 06:30–08:30 h on 3rd October 2019

Total current ¼ IT ¼ 131*:*4 Amps Average voltage ¼ VAver ¼ 52*:*8 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 131*:*4 � 52*:*8 ¼ 6937*:*92 Watts

• Day 18 at 07:00–09:00 h on 6th October 2019

Total current ¼ IT ¼ 125 Amps Average voltage ¼ VAver ¼ 53*:*3 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 125 � 53*:*3 ¼ 6662*:*50 Watts

• Day 19 at 08:00–10:00 h on 7th October 2019

Total current ¼ IT ¼ 121*:*2 Amps Average voltage ¼ VAver ¼ 52*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 121*:*2 � 52*:*4 ¼ 6350*:*88 Watts ¼ 5138*:*25 Watts

• Day 9 at 15:30–17:30 h on 17th October 2019

Total current ¼ *IT* ¼ 108*:*3 Amps Average voltage ¼ *V*Aver ¼ 52*:*4 Volts Power consumed ¼ PCon ¼ *IT* � *V*Aver ¼ 108*:*3 � 52*:*4 ¼ 5674*:*92 Watts

• Day 10 at 14:30–16:30 h on 19th October 2019

Total current ¼ IT ¼ 106*:*9 Amps Average voltage ¼ VAver ¼ 53*:*2 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 106*:*9 � 53*:*2 ¼ 5687*:*08 Watts

• Day 11 at 13:00–15:00 h on 20th October 2019

Total current ¼ IT ¼ 104*:*7 Amps Average voltage ¼ VAver ¼ 52*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 104*:*7 � 52*:*7 ¼ 5517*:*69 Watts

• Day 12 at 19:00–21:00 h on 23rd October 2019

Total current ¼ IT ¼ 160*:*8 Amps Average voltage ¼ VAver ¼ 53*:*7 Volts *Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*

Power consumed ¼ PCon ¼ IT � VAver ¼ 160*:*8 � 53*:*7 ¼ 8634*:*96 Watts

• Day 13 at 18:00–20:00 h on 26th October 2019

Total current ¼ IT ¼ 151*:*1 Amps Average voltage ¼ VAver ¼ 53*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 151*:*1 � 53*:*7 ¼ 8114*:*07 Watts

• Day 14 at 20:00–22:00 h on 27th October 2019

Total current ¼ IT ¼ 144*:*3 Amps Average voltage ¼ VAver ¼ 52*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 141*:*3 � 52*:*4 ¼ 7404*:*12 Watts

• Day 15 at 18:30–20:30 h on 28th October 2019

Total current ¼ IT ¼ 151*:*7 Amps Average voltage ¼ VAver ¼ 53*:*3 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 151*:*7 � 53*:*3 ¼ 8085*:*61 Watts

• Day 16 at 19:30–21:30 h on 2nd November 2019

Total current ¼ IT ¼ 153 Amps Average voltage ¼ VAver ¼ 53*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 153 � 53*:*4 ¼ 8170*:*20 Watts

• Day 17 at 06:30–08:30 h on 3rd November 2019

Total current ¼ IT ¼ 131*:*4 Amps Average voltage ¼ VAver ¼ 52*:*8 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 131*:*4 � 52*:*8 ¼ 6937*:*92 Watts

• Day 18 at 07:00–09:00 h on 6th November 2019

Total current ¼ IT ¼ 125 Amps Average voltage ¼ VAver ¼ 53*:*3 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 125 � 53*:*3 ¼ 6662*:*50 Watts

• Day 19 at 08:00–10:00 h on 7th November 2019

Total current ¼ IT ¼ 121*:*2 Amps Average voltage ¼ VAver ¼ 52*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 121*:*2 � 52*:*4 ¼ 6350*:*88 Watts

• Day 20 at 07:30–07:30 h on 9th November 2019

Total current ¼ IT ¼ 124 Amps Average voltage ¼ VAver ¼ 53*:*6 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 124 � 53*:*6 ¼ 6646*:*40 Watts

• Day 21 at 06:45–08:45 h on 16th November 2019

Total current ¼ IT ¼ 122*:*3 Amps Average voltage ¼ VAver ¼ 52*:*2 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 122*:*3 � 52*:*2 ¼ 6384*:*06 Watts

• Day 22 at 06:00–08:00 h on 17th November 2019

Total current ¼ IT ¼ 127*:*8 Amps Average voltage ¼ VAver ¼ 53*:*4 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 127*:*8 � 53*:*4 ¼ 6824*:*52 Watts

• Day 23 at 07:45–09:45 h on 20th November 2019

Total current ¼ IT ¼ 120*:*7 Amps Average voltage ¼ VAver ¼ 52*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 120*:*7 � 52*:*7 ¼ 6360*:*89 Watts

• Day 24 at 08:15–10:15 h on 22nd November 2019

Total current ¼ IT ¼ 117*:*5 Amps Average voltage ¼ VAver ¼ 52*:*7 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 117*:*5 � 52*:*7 ¼ 6192*:*25 Watts

• Day 25 at 08:15–10:15 h on 26th November 2019

Total current ¼ IT ¼ 128*:*8 Amps Average voltage ¼ VAver ¼ 53*:*3 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 128*:*8 � 53*:*3 ¼ 6865*:*04 Watts

• Day 26 at 08:30–10:30 h on 27th November 2019

Total current ¼ IT ¼ 122*:*1 Amps Average voltage ¼ VAver ¼ 53*:*1 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 122*:*1 � 53*:*1 ¼ 6483*:*51 Watts

• Day 27 at 06:15–08:15 h on 28th November 2019

Total current ¼ IT ¼ 125*:*4 Amps

*Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*

Average voltage ¼ VAver ¼ 53*:*1 Volts Power consumed ¼ PCon ¼ IT � VAver ¼ 128*:*8 � 53*:*3 ¼ 6658*:*74 Watts Total ¼ 170098*:*78 Watts ¼ 170*:*09878 kW ¼ 170*:*1 kW approximation ð Þ Number of hours for 27 days ¼ 27 � 2 ¼ 54 h KWH ¼ 170*:*1 � 54 ¼ 9185*:*4 kWh #60 ¼ 1 kWh 9185*:*4 kWh ¼ #9185*:*4 � 60 ¼ #551124

**3.3 Optimization of the established high power consumed by the modules of the cell site to a minimum value**

Minimize P = X + 13.3Y SUBJECT TO X + 13.3Y ≤ 5764.50 7X + 16Y≤ 5707.91

where P is the minimum power consumed by the cell site; X is the day the power is consumed in the cell site; Y is the hour the power is consumed in the cell site.

>> % OPTIMIZING ENERGY EFFICIENCY THROUGH REDUCTION OF POWER CONSUMPTION IN A TELECOMMUNICATION BASE TRANCEIVER STATION (BTS) SITE USING MACHINE LEARNING

% % Minimize P = X + 13.3Y % Subject to % X + 13.3Y ≤ 5764.50 % 10X + 14. 3Y≤ 5687.08 % % Where

% P is the minimum power consumed by the cell site %X is the day the power is consumed in the cell site %Yis the hour the power is consumed in the cell site


**Table 1.**

*Power consumed from the characterized cell site under study.*

*Alternative Energies and Efficiency Evaluation*

```
f=[-1;-13.3];
   A=[1 13.3;7 16];
   b=[5764.50;5707.91];
Aeq=[0 0];
beq=[0];
   LB=[0 0];
   UB=[inf inf];
   [X,FVAL,EXITFLAG]=linprog(f,A,b,Aeq,beq,LB,UB)
```
Optimization terminated.

X = 0.0000 356.7444 FVAL = -4.7447e+003 EXITFLAG = 1 >>

**3.4 Designing a machine learning rule base that will monitor the power consumed on the modules and minimize it if high**

#### **Figure 1.**

*Designed machine learning fuzzy inference system that will monitor the power consumed on the modules and minimize it if high.*

*Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*


#### **Figure 2.**

*Designed machine learning rule base that will monitor the power consumed on the modules and minimize it if high.*


#### **Table 2.**

*Details of designed machine learning rule base that will monitor the power consumed on the modules and minimize it if high.*

### **3.5 Training ANN in the designed machine learning rules for reduced power consumption in the cell site, thereby enhancing its network performance**

**Figures 3** and **4** will be implemented in the machine learning to enhance its proper functioning to minimize the power consumption in the cell site to save costs.

#### **3.6 Developing an algorithm that will implement 4, 5, and 6**

1. Identify the much power consumed by the module of cell site.


#### **Figure 3.**

*Trained ANN in the designed machine learning rules for reduced power consumption in the cell site thereby enhancing its network performance.*

**Figure 4.** *Model that resulted in the training.*


*Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*

## **3.7 Developing power consumption model for the network under study based on results obtained**

The power consumption model is shown in **Figure 5** reflecting all the simulations obtained as depicted in **Figures 6**–**9**.

## **4. Results and discussion**

The results obtained using machine learning to minimize power consumption are presented and discussed. In **Figure 1**, we have two inputs of power consumed at the cell site and congestion. It also has an output of results (**Table 1**).

**Figure 2** is a designed machine learning rule base that will monitor the power consumed on the modules and minimize it if high. It monitors the power consumed by the modulus of the cell site and minimizes it when detected high. A comprehensive analysis of the rules is as shown in **Table 2** where details of designed machine learning rule base that will monitor the power consumed on the modules and minimize it if high are tabulated. In **Figure 3**, ANN was trained 10 times in the machine learning five rules to give 50 neurons that look like human brain 10 5 = 50. These neurons mimic human intelligence and do what it is instructed to do.

#### **Figure 5.**

*Developed power consumption model for the network under study based on results obtained. The results obtained after simulation are as shown in Figures 6–9.*

#### **Figure 6.**

*Comparing conventional and machine learning power consumed in cell site in day.*

**Figure 7.** *Comparing conventional and machine learning power consumed in cell site in day 3.*

**Figure 4** is incorporated in the machine learning to enhance its efficacy in terms of reducing the power consumed in the cell site, thereby enhancing the financial status of the site. **Figure 5** is the developed power consumption model for the network under study based on results obtained. The results obtained after simulation are as shown in **Figures 6**–**9**. **Figure 6** shows the comparison between conventional and machine learning power consumed in cell site in day 1 (**Table 3**). In **Figure 6**, the highest conventional power consumed by the cell site is 5764 kW while that when machine learning is inculcated in the system is 4733 kW (**Table 4**). With these results, it signifies that the percentage improvement in the reduction of power consumed in the cell site when machine learning is incorporated in the system in day 1 is 17.9%.

**Figure 7** shows that the highest conventional power consumed in the cell site in day 3 is 5191 kW while that when machine learning is integrated in the system is 4731 kW. This clearly showed that the percentage improvement in power consumption reduction in the cell site when machine learning technique is imbibed in the system in day 3 is 8.9%.

*Optimized Energy Efficiency in a Telecommunication Company: Machine Learning Approach DOI: http://dx.doi.org/10.5772/intechopen.104488*

**Figure 9.** *Comparing conventional and machine learning power consumed in cell site in day 7.*


#### **Table 3.**

*Comparison between conventional and machine learning power consumed in cell site in day 1.*


#### **Table 4.**

*Comparison between conventional and machine learning power consumed in cell site in day.*


#### **Table 5.**

*Comparison between conventional and machine learning power consumed in cell site in day 5.*


#### **Table 6.**

*Comparison between conventional and machine learning power consumption in day 7.*

In **Figure 8**, it is obvious that the highest conventional power consumed in the cell site is 5417 kW. On the other hand, when machine learning is integrated in the system, it reduced drastically to 4448 kW, which is 17.9% power consumed by the cell site reduction (**Table 5**). **Figure 9** symbolizes that the highest conventional power consumed by the cell site is 5708 kW while that when machine learning is introduced into the system is 4687 kW, which is 17.9% better that the conventional approach as regards power consumption reduction in the cell (**Table 6**).
