Management Methods of Energy Consumption Parameters Using IoT and Big Data

Abstract

The continuous monitoring of electrical consumption helps to understand energy expenditure in functional consumption environments, such as campuses. For this reason, this work details the development of a mechanism that can do so, such as a network of sensors that is available in a telemetry system, which is determined to perform the acquisition and analysis of energy parameters. These actions are based on the concepts of Internet of Things (IoT) and Big Data. The acquired data are sent in a virtual local area network (VLAN), which is connected to a database server located in the campus environment, using the IoT concept, through the IEEE802.11/IEEE802.3 standards, so that later the analysis and monitoring of the electrical network can be carried out. For the construction of this prototype, noninvasive current sensors connected to a three-phase meter and a communication card are used to extract data from the meter and send it to the database. In the results, the possibility of specifying 30 energy parameters is obtained, with a packet loss rate equal to zero. With this network of sensors, whatever is in operation, such as low-voltage electrical power transformers, distribution boards, among others, can become intelligent data collection devices, from which information is extracted in real time by telemetry.

1. Introduction

The consideration of the impact of the use of electricity on the progress and economic evolution of a region is decisive and crucial. In this way, in economic costs, energy efficiency strategies and policies must also be formulated to accompany and help reduce electricity consumption [1]. When it is possible and there is the possibility, electrical transformers are observed by complex systems, which means high economic cost or, on the contrary, not being monitored, and there is no local or remote management of the reference variables defined in terms of the behavior of energy consumption [2]. Likewise, the term of electrical energy quality is an important aspect in what refers to the use of the energy supply [2]. In addition, the determination of the quality of electrical energy is defined by a wide variety of phenomena in electromagnetic physics, which are characterized by certain criteria, such as voltage and intensity, at a certain time [2]. Among the primary parameters of power quality, there is active power, reactive power, power factor, and voltage unbalance, among others [3].

Within the campus-type architectures, measurement devices can be arranged, which configure, know, and control energy consumption. The information obtained is useful when what is observed by the behavior of electricity consumption can be captured and processed as information, which must be directed to a management and control center, which can make the data collected available through an information system and visualization for both consumers and providers [1, 4, 5].

On the other hand, ICTs have offered innovative products and services that constantly change people’s way of life [6]. Similarly, the Internet of Things (IoT) has undoubtedly opened a great opportunity, and that is to take advantage of the flexibility and efficiency of digital technology in daily life [4]. In addition, it mixes hardware and software technologies, communication protocols, and different processing technologies [6]. Likewise, it carries out a series of interconnections of equipment, personnel, processes, and data to achieve mutual communication and avoid problems. In this way, IoT can help improve different processes and make them more quantifiable and measurable through the collection and analysis of large amounts of data [6].

On the other hand, the presence of sensors as part of telemetry networks is one of the specifications that drives the development of the IoT in a keyway. These are used to collect and transmit data in real time, thus improving the efficiency and functionality of these IoT environments [7]. For the energy sector, there are four main pillars of IoT in energy saving: the transparency between assets, the monitoring and control of energy consumption at an elemental level, the optimization of energy consumption in real time, and the complete optimization of the system [4]. Likewise, it also includes the production, transmission, and distribution of energy, aspects in which use is made of a great variety of sensors, with which measurement processes are made, and it is through the management of the result of these that it could be possible to decrease in costs as in energy [7]. Furthermore, to improve energy efficiency, it is considered that the share of renewable energy must be increased, and the environmental impacts of energy use must be reduced [7]. In this way, IoT can help the energy sector transform from a centralized energy system to a distributed one with the integration of sensors with connectivity to this technology, that is, managed as a smart grid [3].

On the other hand, it is currently very pertinent to consider the use of IoT and Big Data platforms for the proper processing, management, and analysis of large amounts of data [8]. Likewise, to guarantee that users do not feel overwhelmed by the volumes of information, systems are required that can manage, analyze, and convert said data structure, which is obtained by dynamically processing and extracting an observed system [9]. In addition, in campus-type places, you can have a considerable number of transformers or electrical panels, which, with the integration of sensors, can obtain energy indicators. In this way, it is necessary to use technology, such as IoT and Big Data, among others, which can naturally face these challenges that exist today and, likewise, detect faults that could arise during the transmission or distribution of electricity [5].

The objective is framed in proposing a solution based on IoT and Big Data for the acquisition and analysis of energy parameters through a network of sensors using the IEE.802.11 protocol or the IEE.802.3 protocol. The final sensor network has two measurement devices, which manage to capture 30 variables; voltages, currents, powers, and frequency are among them. In addition, there is a dashboard in which the variables mentioned above can be seen in real time.

2. Background

Electricity demand has increased recently, and with the advancement of technology, many impulsive and nonlinear loads have been widely used in distribution networks. Similarly, the accuracy of energy metering is an important basis for the normal operation of the power grid. At present, not only power supply enterprises attach great importance to the development of electric power measurement technology, but also power users, large factories, and enterprises attach great importance to its development [9]. For example, Neve et al. [4] designed an IoT energy monitoring system, which consists of a PZEM-004 T-module-integrated thermocouple (TC) sensor, SD3004 electric energy measurement chip, and a Wemos D1 ESP8266 mini microcontroller for communication. The measurement data are sent to the database server via MQTT. As a result, they generated graphs in Grafana (dashboard) of voltage, current, and power of an electric furnace to demonstrate the operation. Likewise, Dharfizi [10] designed an IoT-based real-time energy monitoring system. It is composed of Raspberry Pi, which uses RS485 communication and node.js programming language to collect data from industrial energy meters (Schneider EM6400NG and Elmeasure EN8400) existing in the study company and store them locally. Therefore, they used Grafana (dashboard) to generate graphs showing the measured values of voltage, current, power, generated consumption, and current harmonics. Similarly, Tahiliani and Dizalwar [11] designed and implemented a minimalistic smart energy meter consisting of a microcontroller, an ACS712 current sensor, a Wi-Fi module, and an organic light-emitting diode (OLED) display. As a result, they generated graphs of nominal power and power consumption of some household appliances on the ThingSpeak platform, which is limited to connect multiple channels in its free version. In the work presented by them, a multi-interface smart energy meter communication following an IoT approach was proposed and demonstrated [2]. The electric meter is composed of an MSP430 processor, which reads parameters from the power grid, collects data such as voltage and current, and sends them to TM4C (processor) through UART (microcontroller). TM4C (processor) sends the data to nodeMCU through UART (microcontroller), which can perform post MQTT. Finally, the voltage, current, and power data are displayed in bar graphs. As a limitation, the device has internal memory capacity to store consumption data for the last 60 days of operation [12, 13].

In the work developed and specified in the article by Neve et al. [4], it is seen that they developed a module for measuring electrical variables in power transformers using IoT concepts. They used as materials a three-phase meter with the MODBUS RTU communication module, the MODBUS RTU to Transmission Control Protocol/Internet Protocol (TCP/IP) converter, a development board with the IEEE 802.11 module, and noninvasive current sensors. Likewise, to load the related information to the database (NoSQL), it was used the MQTT protocol. As a result, the voltage and current measurements in the three phases are presented graphically, in addition to the measurements of active power, apparent power, reactive power, and the average power factor. All these data were captured in a time interval of approximately 2 hours with a time loop of 5 seconds [12, 13].

Some of the work presented was applied to verify point consumption, which, unlike the work carried out, is focused on macro consumption measurements in campus-type sites.

3. Materials and methods/methodology

3.1 Methodology

To carry out the project, the methodology was divided into the following phases:

Phase I. Requirements analysis: The location of the electrical system in different areas of the campus environment was considered to measure the intensity levels of the wireless local area network (WLAN) systems.

Phase II. Logical design of the solution: Once the measurements of the WLAN systems were carried out, the optimal solution was sought for the acquisition and collection of data from the university campus cabinets.

Phase III. Development of the solution model: The data obtained were recorded in a storage infrastructure (database) on a server locally using the local area network or the IEEE 802.11 network. In addition, Big Data was used to process large amounts of data.

Phase IV. Test and optimization of the resulting model: Real-time data reading and storage tests were developed. In addition, connection tests were carried out between the database and the interface, and results were taken, which were used to optimize the final model.

Phase V. Implementation and testing of the resulting system: The implementation of the system was carried out in the different intelligent sensors that are in the electrical power supply areas of the campus air conditioners. In relation to the above, it was intended to take simultaneous samples to determine and guarantee the operation of the system.

Phase VI. Observation and interpretation of system variables: Observation and interpretation actions were carried out in the visualization interface from the tests of the system implemented in the digital power meters, and final samples were taken.

The system is generally arranged to continuously develop and test the telemetry processes, during certain periods, in the development environment determined for your application; generally, this is defined in a development life cycle of the test process, which can be composed of a set of stages that are predisposed, in which the limits must be appropriate for the correct collection of results [14, 15]. In the current situation, the research and implementation work process focuses on the phases and the development of a telemetry system through the acquisition and analysis of energy parameters based on IoT and Big Data concepts, providing it with a method according to the management of devices that become part of a converged system. For this reason, in addition to the corresponding analysis, it is also recommended to have a test bench for experimental practice.

3.2 Materials and procedures for development

The design of the sensor network of a telemetry system based on the concept of IoT and Big Data for the acquisition of energy parameters is built by hardware and software elements; these elements are presented in the following.

3.2.1 Hardware

One of the main characteristics of the creation of the IoT prototype that make up the sensor network is that it can be used as a final product and, in turn, is easy to move. Likewise, it is made up of the following materials:

• Three-phase meter with MODBUS RTU communication module.

• MODBUS RTU to TCP/IP converter.

• NodeMCU development board.

• Noninvasive current sensors.

• Breakers.

• GX16 connectors.

Source: Adapted from [4].

Table 1 presents the characteristics of the meter arranged for the integration of the IoT measurement sensor.

Main technical parameters
Voltage	Measuring range	380 V/100 V
	Consumo de energía	<1 VA
Current	Measuring range	5 A/1 A
	Energy consumption	<1 VA
Measure class	Active energy	0,5S
	Reactive energy	1S
Power supply	Measuring range	AC/DC 85–265 V
	Energy consumption	< 5 VA
Work temperature	Working range	−10–55°C
Frequency	Measuring range	45–65 Hz
Measurement error margin		±5%

Table 1.

Technical references of the measurement module.

Source: [16].

Table 2 presents the technical specifications of the Modbus RTU to TCP/IP converter for the integration of the IoT measurement sensor.

Main technical parameters
Feeding range	5–36 VDC
Standard interfaces	RS232: 300–460.8 kbps; RS485: 300–230.4 kbps
Network mode	Station/AP/AP + Station
Working mode	Transparent transmission/HTTP Client-Modbus TCP or Modbus RTU
Networking protocol	TCP/UDP/ARP/ICMP/DHCP/DNS/HTTP
Communication	Serial to Wi-Fi or Serial to Ethernet or Wi-Fi to Ethernet
Work temperature	−40–85°C

Table 2.

Modbus RTU to TCP/IP converter technical references.

Source: [17].

Table 3 presents the NodeMCU development board features for IoT measurement sensor integration.

Main technical parameters
Microcontroller	Tensilica Xtensa LX106 (32 BIT)
Interfaces	1 UART, 16 PWM (10 BIT), 2 SPI, 1 I2C, 1 I2S 1 ADC (10 BIT)
Flash memory	1 MB
SRAM	64 kB
Speed (MHz)	160
Wi-Fi	IEEE 802.11 b/g/n
Bluetooth	NO
Supply voltage	5 VDC

Table 3.

Microcontroller technical references.

Source: [18].

3.2.1.1 Measurement device design

The development of the IoT sensor for three-phase measurement consists of the devices mentioned in Table 1, and it is corresponding to the hardware section. Figure 1 shows initially a 3D model of the IoT sensor design with the devices that integrate it and its installation in lowvoltage. Finally, Figure 2 shows a functional IoT device in a distribution board, connected to the current transformers of each phase through polyvinyl chloride cables (PVC) [19, 20].

Figure 1.

3D modeling of the IoT sensor design and corresponding low-voltage installation. Source: prepared by the authors.

Figure 2.

Physical and functional design of the IoT sensor in a distribution board. Source: prepared by the authors.

3.2.2 Software

3.2.2.1 Network simulator

To have a first approximation to the network architecture, the modeling of the IoT sensor network was carried out, in which the power supply elements of the university campus are located using an appropriate network simulator for the case [14, 15], that is, the simulator with the required characteristics and specifications. In addition, a port-based virtual local area network (VLAN) was developed to have easy control of network traffic and better security of the data that are sent from the IoT sensor to the database server. Figure 3 illustrates the scope that can be given to the project, the possible installation of 12 electrical network analyzers, each with its network interface that allows connection through the IEEE802.3 or IEEE802.11 communication protocol, and with capacity for greater installation, interconnected with each other in a campus-type unit. The yellow box represents the database server interconnected to a computer for viewing the data recorded by the IoT sensors from the energy distribution sites. The blue box shows the routing of one of the devices using the IEEE802.3 network, and the green box shows the routing of one of the devices using the IEEE802.11 network. The user can determine what type of connection to the communication network he wants. Figure 4 shows the data collection from the server sent by the IoT sensor, thus giving the user the possibility to review the energy parameter collected separately and in real time. This information is being extracted from each of the points where the energy network analyzers are located and is subsequently being stored in a database dedicated solely to this information [21, 22].

Figure 3.

Visualization of the final model of the network of electrical network analyzers of the university campus. Source: prepared by the authors.

Figure 4.

Visualization of energy parameters distributed individually in a dashboard and previously stored in a database. Source: prepared by the authors.

3.2.2.2 IoT sensor communication mechanism

1. Communication with typical systems currently applied for online electrical measurements using the RS485 interface, two-wire full duplex (with Modbus protocol).

2. Communication with typical external current sensors via portable multiconductor flexible cables composed of two soft copper conductors with individual thermoplastic polyvinyl chloride (PVC) insulation.

3. The meter’s communication with the IEEE 802.11 or IEEE 802.3 system was done using a MODBUS RTU to TCP/IP converter, using the said converter in the slave mode.

4. Communication with the database was completed through NodeMCU with the IEEE 802.11 module and configured as the server mode; it was with the responsibility of consulting the meter and sending the received data to the database.

5. To upload the information to the database, the MQTT protocol was used.

6. The database is managed by a timing system that provides stable write speed and higher read speed (TSDB).

7. To view the energy parameters stored in the database, a dashboard made up of dynamic panels was used.

Source: Adapted from [4].

All IoT sensors communicated through a virtual local area network (VLAN) to increase productivity and data security and did not interact with other devices on another network and vice versa [21, 22, 23].

3.2.2.3 Data reception

Three-phase meters with RS-485 communication, also known as EIA/TIA-485, were used to receive data of all the energy parameters that are directed to the database server, each one connected to its respective voltage and current signals from of electrical distribution cabinets. The information that is obtained, is passed to an information system, that uses TCP/IP transmission protocol like mechanism of network, and using the Node-RED programming tool, that converts data from character to numeric format, as well as it connects all devices at same time, and manages the messages received [21, 22, 23]. The information storage was based on a time-series model, making use of the TSDB database, a database focused on IoT solutions, Big Data, and other technologies that collect a lot of information over time. The general architecture of the measurement system that outlines the communication of the sensor network is shown in Figure 5.

Figure 5.

General architecture of the sensor network. Source: prepared by the authors.

3.3 Materials and procedures for development

Using the data obtained from each meter as information, it is necessary to consider other electrical characteristics to help complement the information captured to give a correct interpretation. The following formula was used.

3.3.1 Offset angle

The phase angle is the representation of the power factor angle; it is obtained by finding the arc cosine of the power factor, as represented the following Equation [4]:

$$\phi ={\cos }^{-1}\left(Pf\right).$$E1

4. Results and discussion

As the main goal for the development of the project, it is proposed to carry out a network of sensors of a telemetry system through the acquisition and analysis of energy parameters based on IoT and Big Data concepts with a proposed solution, which was the electrical distribution cabinets or the power transformers of the university campus, which was determined using the concepts allusive to the management of power and electrical loads focusing on consumption. This section presents the results obtained once the operation process was carried out, together with the results obtained in the field tests, starting from the creation of a network of sensors integrating two electrical distribution panels to which the network analyzer was adapted to them, and through this, they become an intelligent object or smart object.

To make measurements in systems that reflect aspects, such as electrical power and what it represents, it is necessary to look for methodologies and models that, using technology and additional mechanisms, carry out such actions directly or indirectly. It is not uncommon that, today, millions of devices already bring with them a form of data extraction making use of protocol concepts with industrial-type determinations, which makes access to their information very limited, and it is not allowed to manage and analyze them. Likewise, it is not possible to connect a large number of said devices and make them converge between them, omitting the possibility of creating a sensor data collection network. Based on the above, we start from the search or creation of a mechanism that is capable of converting devices that do not have a form of data extraction to a device that does, making use of noninvasive external devices and determining it, as well as an IoT object, looking for the possibility of measuring variables or reference data of its operation in order to store and process said information in the future. On the other hand, it is also important to emphasize that using these mechanisms, it is possible to form an information network making use of telemetry processes collecting a large amount of data and making the devices converge in a database, and subsequently, the analysis can be carried out in this case of the fluctuations of the electrical network.

As already mentioned, the implementation characteristics used in this development are determined using simple concepts applied in the power measurement process, initially defined in two of the electrical distribution boards, which are derived from the campus transformers. For this, a network analyzer is used, which adapts to the necessary conditions for the collection of information. This device allows one to manage the data collected by making use of the network interface; it complements the network, which makes it an intelligent object.

The IoT sensor network in conjunction with the data acquisition system was used to monitor two electrical distribution cabinets on the university campus. The correct operation of this gives the possibility of collecting 31 energy parameters; likewise, validation tests of the different parameters obtained were carried out. The measurement network is fully functional, and each IoT sensor has a different topic as a unique identification for each energy parameter. The data collected are available on the university campus database server, where the different data generated by the sensors can be analyzed and visualized in real time. The visualization of these energy parameters is done using a free-to-use dashboard (open source) called Grafana.

Abbreviations for the energy parameters are presented in Table 4

Abbreviation	Description
VA	A phase voltage
VB	B phase voltage
VC	C phase voltage
VAB	AB phase voltage
VBC	BC phase voltage
VCA	CA phase voltage
IA	A phase current
IB	B phase current
IC	C phase current
PA	A phase active power
PB	B phase active power
PC	C phase active power
PS	Total active power
QA	A phase reactive power
QB	B phase reactive power
QC	C phase reactive power
QS	Total reactive power
PfA	A phase power factor
PFB	B phase power factor
PFC	C phase power factor
PFS	Total power factor
SA	A phase apparent power
SB	B phase apparent power
SC	C phase apparent power
SS	Total apparent power
Phi A	A phase Φ
Phi B	B phase Φ
Phi C	C phase Φ
Phi S	Total Φ
F	Frequency

Table 4.

Abbreviation of energy parameters.

Source: prepared by the authors.

Table 5 presents the data obtained from the voltage of the three phases A, B, and C of the two measurement points located on the university campus during a period of 4 days; however, the measurement system can work constantly until it is determined that the information obtained is relevant. The acronyms of V_A1, V_B1, and V_C1 represent the first measurement point, which is in block 6, floor 3 of the university campus. On the other hand, the initials V_A12, V_B12, and V_C12 represent the second measurement point, which is in the basement of block 6. The values contained in Table 5 are the maximum, minimum, and average value. Likewise, Figure 6 shows the data collected and displayed on the graphical interface (dashboard) in a section of time of 5 minutes.

	Minimum	Maximum	Average
VA1	8.1 V	128 V	97.6 V
VB1	8.1 V	128 V	124 V
VC1	8.1 V	128 V	122 V
VAB1	216 V	223 V	220 V
VBC1	216 V	220 V	219 V
VCA1	217 V	223 V	220 V
VA12	65.5 V	128 V	126 V
VB12	114 V	128 V	126 V
VC12	8.1 V	128 V	118 V
VAB12	215 V	221 V	219 V
VBC12	215 V	222 V	219 V
VCA12	215 V	222 V	219 V

Table 5.

Values of the voltages obtained by the sensors installed in the distribution boards.

Source: prepared by the authors.

Figure 6.

Graphic interface of the visualization of the voltage energy parameters of both the sensors. Source: prepared by the authors.

Figure 6 shows an interactive graph of three-phase voltage measurements on a university campus network at the installation site, where each line and its respective color have their specific abbreviation at the top right of the figure. Likewise, network fluctuations are observed in the measurements of both the sensors.

Similarly, Table 6 shows the values of maximum, minimum, and the average of the normalized currents from 0 to 5 A of phases A, B, and C of the two measurement points; Figure 7 shows an interaction graph of the three-phase current measurements recorded by the sensors during a time window of 5 minutes, where each color of the signal is associated with an abbreviation located in the upper right part of the image. The IoT sensor located in the basement counts current transformers with a ratio of 1000 A to 5 A. In addition, the IoT sensor located on floor 3 of the same block has current transformers with a 600 A to 5 A ratio.

	Minimum	Maximum	Average
IA1	0.06 A	0.21 A	0.08 A
IB1	0.02 A	0.22 A	0.04 A
IC1	0.02 A	0.31 A	0.09 A
IA12	0 A	0.44 A	0.05 A
IB12	0 A	0.47 A	0.05 A
IC12	0 A	0.41 A	0.03 A

Table 6.

Electric current values obtained by the measurement sensors.

Source: prepared by the authors.

Figure 7.

Graphical interface for the visualization of the current variations of both the sensors. Source: prepared by the authors.

On the other hand, Table 7 shows the active powers with their maximum, minimum, and average value of phases A, B, and C of the two measurement points. Figure 8 shows the three-phase active power consumption levels recorded by the sensors in a time section of 5 minutes; in addition, during the measurements, it was observed that the active power consumption is high when the university campus is in academic activities.

	Minimum	Maximum	Average
PA1	1 W	56.4 W	26.8 W
PB1	0.08 W	9.5 W	3.1 W
PC1	3.5 W	36.8 W	20.2 W
PA12	0 W	51.4 W	7.5 W
PB12	0 W	44 W	6.2 W
PC12	0 W	7 W	0.47 W

Table 7.

Values of the active powers obtained by the IoT sensors.

Source: prepared by the authors.

Figure 8.

Graphic interface for displaying the active power variations registered by each sensor. Source: prepared by the authors.

On the other hand, when reviewing the results presented in Tables 6 and 7, it can be seen that the energy consumption in some buildings and in a place like in the basement of block 6 is higher than the consumption in floor 3 of the same block. Likewise, it can be seen that consumption on floor 3 is constant; therefore, its average is higher compared to that of the basement. This information can also be seen in Figures 7 and 8, which correspond to the currents and active powers of the sensors in a 5-minute time section.

Likewise, Table 8 shows the reactive powers with their maximum, minimum, and average values of phases A, B, and C of the two measurement points. Figure 9 presents a 5-minute section of the reactive power obtained by the three-phase measurement IoT sensors during the entire measurement period; this power is related to the existence of coils or capacitors in the electrical installation associated with the distribution boards of the university campus.

	Minimum	Maximum	Average
QA1	0.37 VAR	54.6 VAR	26.9 VAR
QB1	0.51 VAR	24.3 VAR	11 VAR
QC1	0.74 VAR	53.2 VAR	31.1 VAR
QA12	0 VAR	24.1 VAR	3.37 VAR
QB12	0 VAR	33.2 VAR	5.18 VAR
QC12	0 VAR	47.5 VAR	7.60 VAR

Table 8.

Values of the reactive powers obtained by the IoT sensors.

Source: Prepared by the authors.

Figure 9.

Graphic interface for displaying the active power variations registered by each sensor. Source: prepared by the authors.

Similarly, Table 9 shows the apparent powers with their maximum, minimum, and average values of phases A, B, and C of the two measurement points. Figure 10 shows in a 5-minute section the interactions of the three-phase measurements of apparent power obtained by the sensors during the entire measurement period; this power is the sum of the energy that transforms these circuits in the form of heat.

	Minimum	Maximum	Average
SA1	0.72 VA	79.3 VA	38.6 VA
SB1	0.34 VA	27.8 VA	13.2 VA
SC1	3.13 VA	65.2 VA	37.5 VA
SA12	0 VA	55 VA	8.62 VA
SB12	0 VA	53.2 VA	8.20 VA
SC12	0 VA	51.6 VA	7.13 VA

Table 9.

Apparent power values obtained by the IoT sensors.

Source: prepared by the authors.

Figure 10.

Graphic interface for displaying the active power variations registered by each sensor. Source: prepared by the authors.

Table 8 corresponds to reactive power indicating that the sensor located on floor 3 of block 6 obtained higher reactance values compared to the meter located in the basement of the same block. In addition, Table 9 shows the apparent power values where there was likewise greater apparent power in the electrical cabinet located on floor 3 of block 6.

Equally important, Table 10 shows the power factors with their maximum, minimum, and average values of phases A, B, and C and the total of the two measurement points. Likewise, Table 11 shows the phase angle of each meter.

	Minimum	Maximum	Average
Pfa1	0.685	0.940	0.688
Pfb1	0.0277	0.378	0.202
Pfc1	0.04	0.604	0.527
Pfs1	0.04	1	0.891
Pfa12	0.087	1	0.97
Pfb12	0.11	1	0.934
Pfc12	0.037	1	0.823
Pfs12	0.099	1	0.885

Table 10.

Values of the power factors obtained by the IoT sensors.

Source: prepared by the authors.

	Minimum	Maximum	Average
Phi A1	45.76°	19.94°	46.52°
Phi B1	88.41°	67.79°	78.35°
Phi C1	87.71°	52.84°	58.19°
Phi S1	87.71°	0°	27°
PhiA12	85°	0°	14.07°
Phi B12	83.7°	0°	20.93°
PhiC12	87.88°	0°	34.61°
Phi S12	84.26°	0°	27.75°

Table 11.

Values of the offset angles.

Source: Prepared by the authors.

Table 10 presents the power factor values for both the sensors; in the IoT sensor located in the basement of block 6 of the university campus, a maximum power factor value equal to one was presented; this is related to the time in which there was no electricity consumption. In the same way, from Table 11, it is possible to observe the values of the lag angles, giving an answer from another perspective that relates the apparent power and the active power, in other words, the lag in degrees existing between the intensity of the current and the voltage or voltage in the alternating current circuit.

The processing of energy indicators refers mainly to the support received from energy providers and the support and management of these parameters for the generation and transformation of electrical energy, as is the case of electrical powers, which can be seen in Table 12, which represents the maximum, minimum, and average values of the total powers (active, reactive, and apparent). Figure 11 shows the measurements obtained by the IoT sensors of the total powers (active, reactive, and apparent); these data are provided directly by the meters.

	Minimum	Maximum	Average
PS1	0.365 W	54.6 W	27.7 W
QS1	0.508 VAR	23.7 VAR	11 VAR
SS1	0.742 VA	53.2 VA	31.7 VA
PS12	0 W	24.1 W	3.3 W
QS12	0 VAR	33.2 VAR	4.68 VAR
SS12	0 VA	47.5 VA	6.89 VA

Table 12.

Values of the total powers (P_S, Q_S, S_S) obtained by the IoT sensors.

Source: prepared by the authors.

Figure 11.

Graphic interface for the visualization of the total power variations (active, reactive, and apparent) of the sensors. Source: prepared by the authors.

To evaluate the performance of the data transmission, the Wireshark program was used, which performs the analysis of protocols and data and allows visualizing the traffic that is happening on the network. The collection and sending of data from the IoT sensors, are scheduled every 5 seconds and are segmented into an average of 25 sections, as can be seen in Table 13, which shows the information obtained from the energetic measurements.

Number of measurements	Absolute time	Time	Source IP address	Destination IP address	Segmental latency	Total latency	Protocol	Size (Bytes)	Information publication message
280	30/11/2021 4:57	0.980271323	172.16.26.103	172.16.161.144	0	0.637971015	MQTT	77	va
284	30/11/2021 4:57	0.989446441	172.16.26.103	172.16.161.144	0.009175118		MQTT	193	vb, vc, vab, vbc, vca, ia
291	30/11/2021 4:57	1.011690827	172.16.26.103	172.16.161.144	0.022244386		MQTT	75	ib
299	30/11/2021 4:57	1.033896213	172.16.26.103	172.16.161.144	0.022205386		MQTT	75	ic
303	30/11/2021 4:57	1.056793208	172.16.26.103	172.16.161.144	0.022896995		MQTT	78	pa
306	30/11/2021 4:57	1.078475087	172.16.26.103	172.16.161.144	0.021681879		MQTT	77	pb
308	30/11/2021 4:57	1.101522884	172.16.26.103	172.16.161.144	0.023047797		MQTT	78	pc
311	30/11/2021 4:57	1.123173163	172.16.26.103	172.16.161.144	0.021650279		MQTT	78	ps
319	30/11/2021 4:57	1.156627794	172.16.26.103	172.16.161.144	0.033454631		MQTT	78	qa
321	30/11/2021 4:57	1.178479375	172.16.26.103	172.16.161.144	0.021851581		MQTT	77	qb
323	30/11/2021 4:57	1.200369057	172.16.26.103	172.16.161.144	0.021889682		MQTT	78	qc
336	30/11/2021 4:57	1.222415441	172.16.26.103	172.16.161.144	0.022046384		MQTT	78	qs
338	30/11/2021 4:57	1.245752241	172.16.26.103	172.16.161.144	0.0233368		MQTT	76	pfa
355	30/11/2021 4:57	1.268541235	172.16.26.103	172.16.161.144	0.022788994		MQTT	76	pfb
358	30/11/2021 4:57	1.291170826	172.16.26.103	172.16.161.144	0.022629591		MQTT	76	pfc
380	30/11/2021 4:57	1.313146809	172.16.26.103	172.16.161.144	0.021975983		MQTT	76	pfs
386	30/11/2021 4:57	1.346678341	172.16.26.103	172.16.161.144	0.033531532		MQTT	78	sa
404	30/11/2021 4:57	1.367970615	172.16.26.103	172.16.161.144	0.021292274		MQTT	77	sb
414	30/11/2021 4:57	1.390034699	172.16.26.103	172.16.161.144	0.022064084		MQTT	78	sc
417	30/11/2021 4:57	1.411740979	172.16.26.103	172.16.161.144	0.02170628		MQTT	78	ss
428	30/11/2021 4:57	1.444022294	172.16.26.103	172.16.161.144	0.032281315		MQTT	75	f
437	30/11/2021 4:57	1.46624498	172.16.26.103	172.16.161.144	0.022222686		MQTT	78	vdesb
444	30/11/2021 4:57	1.487915959	172.16.26.103	172.16.161.144	0.021670979		MQTT	78	idesb
446	30/11/2021 4:57	1.520016373	172.16.26.103	172.16.161.144	0.032100414		MQTT	83	Angulo_fpa
450	30/11/2021 4:57	1.553387902	172.16.26.103	172.16.161.144	0.033371529		MQTT	83	Angulo_fpb
453	30/11/2021 4:57	1.58583512	172.16.26.103	172.16.161.144	0.032447218		MQTT	83	Angulo_fpc
456	30/11/2021 4:57	1.618242338	172.16.26.103	172.16.161.144	0.032407218		MQTT	83	Angulo_fps

Table 13.

Segmentation of data transmission.

Source: Prepared by the authors.

In addition, communication tests were carried out in sending and confirming packages for approximately 58.82 hours between the IoT sensors and the database server in a private network or VLAN, in order to verify the sending and receiving of data from where the results presented in Tables 14 and 15 were obtained, which means that in the 58.82-hour interval of sending and receiving information, there was no loss or forwarding of packets, which provides good scalability and flexibility in the network.

Parameters	Value
Total packages sent	994,618
Total packets received	994,618
Total discarded packages	0
Bytes sent from server to IoT sensor	77 MB
Bytes sent from IoT sensor to server	51 MB
Average latency	23.20 ms

Table 14.

Results of the data transmission performance of the meter block 6 third floor.

Source: prepared by the authors.

Parameters	Value
Total packages sent	883,251
Total packets received	883,251
Total discarded packages	0
Bytes sent from server to IoT sensor	71 MB
Bytes sent from IoT sensor to server	47 MB
Average latency	20.70 ms

Table 15.

Performance results of meter block 6 basement data transmission.

Source: prepared by the authors.

The total information of the packet traffic in 1-second intervals during the measurement time had a duration of 58.82 hours; to visualize the information in a more detailed way, Figure 12 shows the traffic analysis information in a window of duration of approximately 1.67 minutes; the time spaces between the peaks represent intervals when the IoT sensor is not communicating at all with the database server; for that reason, the packets delivered are 0. In addition, each signal peak refers to the number of packets that are sent and describes the number of sequences created during the transmission of information, that is, the number of measurements (280), (284) and (291) shown in Table 13, corresponds to eight of the thirty-one variables. In that case, the measurement the value two hundred and eighty (280), is the information corresponding to the voltage in phase A that it was sent, this is the measurement corresponding to the first packet sent; in the measurement (284), it was sends the information contained from the voltage in phase B to the current in phase A; likewise, this is done with the measurement (291), this information is corresponding to the current in phase B is sent. It should be noted that the data frame is not it ends until the last measurement number packet (456) has been sent, which corresponds to the variable “angle_fps”. Finally, the duration time of each data frame corresponds to an average of 0.63 seconds and is composed of the time intervals when the sensor sends information and when the sensor does not share information with the database server.

Figure 12.

Traffic analysis graph of the measurement for 58.82 hours approximately in an interval of 1.67 minutes. Source: prepared by the authors.

This is the mature concept of a useful telemetry mechanism suitable for an interpreter who is familiar with electrical power consumption mechanisms to be able to perform, analyze, and interpret the information collected alluding to energy consumption. Therefore, those who are interested in this part know that it must be conditioned or improved and, in that case, must regulate the consumption generation processes; with this, a certifiable concept is allowed and is also supported by the green consumption standards of energy and by the ISO50001 standards that currently govern in Colombia.

There are currently different types of electrical network analyzers on the market, for which costs vary depending on customer needs. The designed modules prototypes, convert power transformers and the derived electrical distribution boards, into a class of smart object, and this allows to generate a network of sensors of low cost and with a reliable and safe data transmission method, in other words, when these components are in operation, there is not will be loss of information. Likewise, the customer can determine the measurement time required for acceptable results, and which electrical variables they want to acquire and know, furthermore, and if they want to use the transmission method remotely using the IEEE802.11 standard, or locally using the IEEE802.3 standard. In addition, the end customer can view the energy parameters through a graphical interface (dashboard) in a dynamic and interactive way.

The sensor network during the development of this work has a database server hosted on the university campus, which has a wide storage limit, for the integration of more sensors and the massive collection of more energy parameters having remote access.

5. Conclusions

According to the results obtained with the operation of the sensor network based on the concept of IoT and Big Data, the following advantages could be evidenced:

• The method of data acquisition, transport, and processing of energy parameters used satisfactorily achieved acceptable performance, achieving zero loss of information packets as well as latency with permissible response time.

• The network of energy measurement sensors is convergent in all its measurement sensors, which indicates that the architecture of this network is functional.

• The database server developed for the storage of information supports the processes based on IoT and Big Data, which allows us to have a faster response time to visualize the energy parameters captured by the sensors based on time.

• The devices developed allow the user easy handling and installation. Likewise, it is multifunctional, which means that it can measure not only three-phase voltages but also single- and two-phase voltages.

• The results suggest that the structured sensor network does not require a large bandwidth for the transmission of information.

• The proposed sensor network is in the investigative exploration stage and, in turn, in this work, can serve as a guide for generating feedback and developing future projects based on this work.

• To advance the project, other communication mechanisms, such as low-frequency laser radio, must be considered, as this allows a greater range of connectivity and communicates devices, where the IEEE802.11 and IEEE802.3 standards are not present.

• The work carried out has two measurement devices; it is recommended in the future to carry out the measurement processes using four or more devices that converge in the proposed data acquisition system. Likewise, make a robust analysis of the data obtained using Big Data processes to be able to make predictions in the future.

Acknowledgments

Thanks to the Santiago de Cali University, to the COMBA I+D group, for the sponsorship and support for the development of this research work.