Table 1 below provides information on the electronic components used.

To design GIC application, the name given to the desktop application developed, we used Dart, a programming language, in Flutter FrameWork to develop the platform, and Figma enabled us to implement the application model.

This study required the development of a working methodology. It covers various aspects such as the choice of electronic components, the calibration of sensors and the implementation of the device.

To ensure the accuracy of the values, we calibrated the wattmeter. We applied the known voltage, current, load and frequency to the wattmeter, then compared the measurements displayed with those of the reference devices (the standard voltmeter and ammeter). If the differences exceeded the tolerances, we adjusted the parameters of the wattmeter using its potentiometer.

Three players are involved in the project ecosystem. These are the customer, the supplier and the meter itself. The customer recharges his meter using his mobile phone. Electricity will then be available at the customer’s home, and at the same time, consumption data will be sent to a database via the GSM network for viewing by the supplier on a platform ( Figure 2 ).

Initially, the subscriber sends money to recharge his meter using his mobile phone. Thanks to the GSM SIM 800L module, the ESP 32 processes the message and converts the amount into a given quantity of kilowatt-hours. The subscriber then has access to the electrical energy in his concession. The consumption data for the installation is measured by the PZEM-004T module, displayed on the LCD screen and sent to a database for viewing by the supplier on the GIC platform. The system is equipped with an SD reader that holds a memory card. The data is stored on this memory so that it is not lost in the event of a power cut. The relay also switches the customer’s installation off and on again. The metering system is summarized in the following diagram. This diagram includes all the electronic components that interact in the system ( Figure 3 ).

The type for electrical connections was made using the Proteus software ( Figure 4 ).

Figure 5 represents the operating diagram with actions per step.

A number of electrical parameters come into play to ensure that the system operates correctly. While some are measured directly by sensors, others are calculated using theoretical formulae.

1) Measured parameters

Here, the sensors provide us via the processing unit with electrical parameters such as the instantaneous voltage ϑ(t) and current i(t).

2) Determined parameters

Some parameters are not directly supplied by the sensors. Theoretical formulas taking into account the measured parameters are therefore used to determine these parameters. They are updated at each period T, where T is the period of the alternating current signal (in seconds).

$\begin{array}{c}{V}_{eff}=\sqrt{\frac{1}{T}\underset{0}{\overset{T}{{\displaystyle \int }}}{\vartheta }^2\left(t\right)\text{d}t}\end{array}$ (1)

$\begin{array}{c}{I}_{eff}=\sqrt{\frac{1}{T}\underset{0}{\overset{T}{{\displaystyle \int }}}{i}^2\left(t\right)\text{d}t}\end{array}$ (2)

with $V_{eff}$ and $I_{eff}$ the rms voltage and current respectively.

This power p(t) is obtained by the product of the instantaneous values of voltage ϑ(t) and current i(t):

$\begin{array}{c}p\left(t\right)=\vartheta \left(t\right)\times \text{i}\left(t\right)\end{array}$ (3)

Real power P, also known as active power, is the average of the instantaneous power over a complete cycle of the waveform. It is measured in watts (W):

$\begin{array}{c}P=\frac{1}{T}\underset{0}{\overset{T}{{\displaystyle \int }}}p\left(t\right)\text{d}t\end{array}$ (4)

It is also determined by:

$\begin{array}{c}P=V_{eff}\times I_{eff}\times \cos \left(\varphi \right)\end{array}$ (5)

$\cos \left(\varphi \right)$ is the power factor, which is the cosine of the phase angle $\varphi$ between the voltage and the current.

Apparent power is measured in volt-amperes (VA) and is calculated as follows:

$\begin{array}{c}S=V_{eff}\times I_{eff}\end{array}$ (6)

The power factor is the ratio of real power to apparent power:

$\begin{array}{c}FP=\frac{P}{S}\end{array}$ (7)

The instantaneous energy e(t) is obtained by the product of the instantaneous values of the voltage ϑ(t), the current i(t) and the time t or by the product of the instantaneous power and the time t:

$\begin{array}{c}e\left(t\right)=\vartheta \left(t\right)\times \text{i}\left(t\right)\times t\end{array}$ (8)

$\begin{array}{c}E=\frac{1}{T}\underset{0}{\overset{T}{{\displaystyle \int }}}e\left(t\right)\text{d}t\end{array}$ (9)

GIC is a desktop application developed to enable electricity network providers to view their customers’ electricity consumption in real time. When the GIC application is opened, the interface that appears allows the manager to self-identify for the first time by entering their email address and a password ( Figure 8 ). The manager then logs on to access the interface providing information on the status of subscribers’ meters, as shown in Figure 9 .

Click on ‘‘Add” to display the following interface. This is used to register a new subscriber who wants to buy a meter from the supplier. The subscriber provides a few personal details, which the administrator in charge of the application will enter to register them ( Figure 10 ).

The “History’’ section traces the various top-ups made by each subscriber from the first top-up when the meter was requisitioned to the last top-up. The following interface is displayed when you click on “Dashbord’’. It shows the total power curve of subscribers by day. This will enable suppliers to see the demand from subscribers on a daily basis, so they know whether they need to increase the power of their plants ( Figure 11 ).

The system is tested under real conditions to assess the accuracy of the values of the electrical parameters it displays. To do this, when the meter is switched on, we first connect an electric lamp to its output and then a PC computer. The loads are then each connected to the wattmeter to validate the measurements. The results obtained are shown in Table 2 .

*fan, lamp and refrigerator used together.

First of all, we can see that the current demand has increased because the power of the computer is much greater than that of the electric lamp. The uncertainty values are all less than 3%, which complies with ISO/IEC 17025: 2017, which specifies a value of less than 5% [26] . So, when we take a look at existing work, we see that even if the uncertainty values comply with the standard, most of these works have values in excess of 3%. These results show that our smart meter accurately measures electrical parameters. In addition, characters are predefined to allow subscribers to interact with the meter using their mobile phones ( Table 3 ).

For example, when a customer wants to switch off their meter, they simply send “0” as a character. In return, they will receive the confirmation message ‘‘meter disabled’’ and all their loads will be switched off.

The GIC application dashboard shows us the curve observed in Figure 12 after the various tests carried out on the electricity meter.

The electric lamp and the computer each remained connected to the counting device that we developed for an approximate period of one hour. The analysis of the energy consumption curve associated with these two loads reveals a virtual absence of variation, with the consumption remaining practically zero, which is reflected in zero-sloped segments (horizontal asymptotes) on the graph. On the other hand, sudden increases in the level of credit (vertical asymptotes) correspond to recharging operations: each injection of credit induces an instantaneous increase in the value of the available kilowatt-hour. The result is a “stepped” representation of consumption, as displayed in the GIC application dashboard, that is both consistent and self-explanatory ( Figure 13 ).

The current design of the smart electricity metering system, while effective for measuring and transmitting consumption data, has some potential vulnerabilities that are worth investigating to enhance the robustness and reliability of the device in a real-world environment ( Table 4 ).

A few months later, after recharging the 1500 kWh meter—bringing the remaining credit from about 19 kWh to 1519 kWh—we left a refrigerator with an electric lamp running continuously for about three hours. Observation of the remaining credit curve shows a marginal decrease (from 1519 kWh to 1518 kWh), reflecting limited electricity demand and therefore low energy consumption ( Figure 14 ).