Evaluating the Performance of Smart Meters: Insights into Energy Management, Dynamic Pricing and Consumer Behavior

Abstract

Energy consumption demands are rapidly increasing every year, with an 8% annual growth rate projected for the next five years. As buildings represent over 35% of this demand, a metering system is required for monitoring to accurately calculate costs. This paper explores the evolution and impact of energy management through smart meters, emphasizing their superiority over traditional electromechanical devices, in applications such as minimizing power losses and enhancing grid reliability. This study compares the performance of five distinct metering systems, including electromechanical and advanced smart meters. Real-time testing across various scenarios is incorporated, examining parameters such as real and reactive power measurement, accuracy and adaptability to smart grids. Key findings revealed that smart meters, notably the EDMI Mk10A, outperform legacy systems in precision, data transmission and energy optimization. In addition, the potential of smart meters to enable dynamic cost calculation and prevent electricity theft is evident. Despite their advantages, challenges such as data privacy, installation costs and electromagnetic radiation concerns, persist. Future investigations to address the identified limitations are required.

1. Introduction

The demand for greener energy and minimization of fuel waste is leading towards the pact for decarbonization and the exploitation of renewable energy sources. An annual 8% increase in energy consumption is expected for the next five years, which requires adequate infrastructure to provide the energy required, in an eco-friendly form [1]. As commercial and industrial buildings represent over 35% of the total electricity utilized, with energy costs continuously rising, a proper metering system to monitor, record the consumed energy and the power quality of the grid is essential while minimizing the power losses.

From the introduction of the first meter mechanism by Thomas Edison in late 1800s, up to the hydrogen meter by Siemens–Shuckert, rapid advancements in power meter technology throughout the years are evident [2]. The first analog electricity meter with an aluminum disk and bars, developed by Elihu Thomson, became a status quo in the energy metering system and even received an award in Paris in 1890. It was adopted as the ideal metering module that could read both single-phase and three-phase power directly or through current transformers. In addition, both DC and AC power could be measured.

Within the last 50 years, power meters, developed by Landis and Gyr based on the Thomson wattmeter, have been exploited [3]. Their operation is based on the revolutions of the rotating disk that is moved by two coils: an M-shaped shunt electromagnet coil connected in parallel and a U-shaped current coil connected in series with the load. The driving force caused from the flux produced by the two magnets makes the pivotal bearing move. As a result, a disk, connected to this implementation, is triggered to rotate. This component is controlled by a braking magnet for further setting and speed adjustments. The schematic and the layout of the power meter are presented in Figure 1 [3,4].

Analog meters are affordable, easy to install and durable, with only a 3% failure rate [5]. However, they lack both accuracy and convenience for measurements and require the employment of extra workers, while being more prone to interference and thus vulnerable to energy theft. Usually, these practices are based on certain illegal moves that aim to reduce or even stop the rotation of the moving disk entirely, by altering the flux with external magnets or interrupting the cable connection. In addition, one major drawback is the access to the meter in case of a fault diagnosis, or the cut-off process if an electricity theft is confirmed.

To overcome these issues, the adoption of smart meters is recommended. Smart meters introduce a variety of advantages, including the following:

• Remote fault, control and operation;
• Wireless data transmission;
• Precise and real-time monitoring of energy usage;
• Availability for frequent measurements, up to 15 min, during the day;
• Implementation of dynamic energy billing tariffs;
• Automation of smart home appliances operation;
• Bidirectional energy metering and self-generation tracking [2,3,6].

Smart meters increase power system reliability by optimizing energy source utilization, ensuring an uninterrupted power supply, enabling data logging and reducing power losses through impedance and topology estimation [2,7]. Since these are monitoring and transmitting data remotely in real time, the power resource efficiency is increased. This is an automated operation of the appliances, in order to shift the load when energy prices are low, hence avoiding surge pricing during peak hours [8]. Statistical data can be provided for consumer behavior purposes, so users can employ flexible energy management practices to reduce costs. In addition, providers may take advantage of smart meters to reduce operation costs by over 10% due to load shifting, while also reducing stress on the grid, thereby minimizing faults [9].

Although smart meters establish several benefits, certain short-term mainly challenges should be addressed. The installation of the modules is quite challenging; for example, in Greece, 95% of the meters are electromechanical and need to be swapped [10]. The large amount of data transmitted by the smart meters need precise handling and safeguarding as the privacy of customers is not negotiable. In addition, massive data centers, with high energy demands, are required to store all the information, making them vulnerable to cyber-attacks. This concept is defined as the Internet of Energy, an interconnected network for smart management between the generation, transmission and distribution systems [11]. Therefore, data management and transmission (up to 3 TB annually by each meter) without bandwidth loss is essential for ensuring Quality of Service (QoS) by the aggregator of the distribution system [12].

• A typical smart meter schematic, along with its layout, illustrating the three different parts (modem, contactors and chassis) is shown in Figure 2 [2,13].

As mentioned above, electricity theft has to be addressed, as economic losses are vast, reaching over 100 billion dollars in the US alone, while causing high degradation in power quality [14]. Incentives such as tariffs for low energy costs at off-peak hours or weekends will enable the consumers to avoid theft attempts. The utilization of smart meters takes advantage of this scheme, as the time frame for data logging is fast and dynamic pricing in real time can be achieved [15,16]. It also manages the integration of smart grid schemes, as each consumer can install renewable energy sources, mainly solar, at their homes, to produce their own power for self-consumption, charge electric vehicles, or supply energy back to the grid. Hence, the monitoring of produced, consumed and grid-supplied energy is enabled, and a reduction in greenhouse emission is achieved, reaching savings of over 12% [17,18]. A typical smart grid scheme is presented in Figure 3.

Usefulness, ease of use, and remote control are key factors for the consumers’ acceptance and adoption of a smart meter [19]. Energy savings and adaptability to the smart grid system also have a significant influence towards that goal. However, certain users do not comply with this rule, as there are concerns about data privacy and radiation through the mobile communications network that smart meters utilize, affecting the behavior of the consumers [20]. This matter needs to be addressed, reinforcing the transition to smart metering and the era of smart grid applications.

The goal of this paper is to test and validate the performance of five different arrays: an electromechanical meter, two smart meters, a digital analyzer and a digital multimeter. Through this experiment, the real and reactive power of a modern industrial building will be measured simultaneously by the modules in order to assess the benefits of smart meters, including accuracy, consumer behavior and the potential risks of their adoption, such as the radiation caused by 4G communication. The communication protocols that can be applied in smart metering systems to ensure data privacy, robustness and user electricity consumption behavior will also be reviewed.

The rest of this paper is structured as the follows. The research methodology is assessed in Section 2. The findings of the analysis are summarized in Section 3, followed by the discussion, while Section 4 concludes the article. Here, this study’s shortcomings are discussed, and directions for future work are proposed.

2. Materials and Methods

2.1. Experimental Layout

According to the experiment requirements, various electromechanical and smart meters were tested for direct comparison. Installation was conducted on an existing company site, titled “H.E.C. Systems”, using a three-phase supply with 25 kVA apparent power, monitored via a 400 V, 10–60 Amp meter (ABB, Zurich, Switzerland). All components were installed inside the distribution panel after the main breaker to measure the total energy that is consumed, so the panel layout is presented in Figure 4.

Two different schemes were applied: direct connection to the meter and connection through current transformers, depending on the power required, as shown in Figure 5 [21].

The meters utilized, electromechanical, digital or smart, were 5 in total, with distinct specifications. All three meters were 3 phase, and the information provided on their manufacturer labels is summarized in

Table 1. Meters Specifications.

Meters	Landis&Gyr MM2600	EDMI Mk10A	ELControl Microvip3	ABB DMTME	Zennio KES-Plus
Ref.Voltage (V)	230/400	230/400	600	550	230
Range (V)	N/A	0.8–1.15 Un *	N/A	10–500	100–230
IMAX (A)	10–60	100	1000	5	120
Accuracy Class	2	1	1	1	B
Consumption (VA)	N/A	<0.5	4	0.45	0.413
Connectivity	N/A	RS232, IP, MOD	N/A	N/A	KNX
Temp.Range (°C)	−20 to 50	−25 to 60	−10 to 50	0 to 50	0 to 40
Protection Class	N/A	CAT II	CAT III	N/A	CAT II
IP Rating	IP20	IP53	IP40	IP50	IP20
Weight (kg)	3	2	2.9	0.32	0.10
Dimensions (mm)	260 × 150 × 130	210 × 166 × 74	251 × 239 × 104	105 × 90 × 63	67 × 90 × 35

* Nominal voltage.

[22,23,24,25,26].

The MM2600 (Landis&Gyr, Cham, Switzerland), a typical electromechanical meter manufactured in 2002, measures only active power, with every 75 rotations standing for 1 kilowatt-hour, while the Mk10A (EDMI, Singapore), introduced in 2014, measures each kWh with 500 impulses, which can be observed through the led activation. Both meters are directly connected to the load. The Atlas model is attached to a GSM modem with Sim card for data transmission to the Independent Power Transmission Operator (IPTO or ADMIE) every 15 min on a daily basis. It may also operate through GSM at a 850–1900 Mhz frequency range at a 100 ms time interval, with a typical consumption of 2 watts via the IEC 62056 protocol, using the port number 12212 [27]. The Sim card cannot be removed, and a possible breach triggers an alarm at the operator’s end, while the seal on the MM2600 can be removed, requiring personnel to check it during counting. The layout of the two meters is depicted in Figure 6 below.

2.2. Measurements Validation

The EDMI smart meter can record the current and voltage for each phase, as well as the power factor. In addition, incoming and outgoing energy is monitored including the differentiation in values between real, apparent and reactive power, presented in Figure 7, a value that the most digital meters are unable to compute.

In order to test and validate the measurements by the meters, two additional metering devices were installed. A digital analyzer by ELcontrol (NIFA Electronics, Ahmedabad, India) and the DMTME digital 3 phase energy meter (ABB, Switzerland) function as multi-interface components. The analyzer is connected via clamp-on ammeters rated at 1000 A/1 Volt Rms to measure power, while the meter requires current transformers that are suspectable to digital interference. Both tools can measure and store the maximum, minimum and mean values of the monitored parameters, as well as real and reactive power per phase with high accuracy and efficiency. Both tools are presented in Figure 8.

The final component installed and tested was the Zennio KES Plus (Zennio, San Francisco, CA, USA) digital 3 phase energy meter with KNX protocol compatibility. This layout can measure the energy produced and load demand of the application, as well as the cost, different pricing schemes and CO2 emissions with the proper configuration. In addition, a plethora of indicators and sound notifications are available in the module for constant information of the consumer. The user interface, displayed in Figure 9, has great potential and many settings, allowing for better optimization.

3. Results and Discussion

A total of 25,000 measurements were gathered over a period of 3 months, from 5 September to 5 December 2024, in the central part of Athens, Greece. The testing revealed that all five modules exhibit a small deviation on the computed values, which can prove to be important in determining the accuracy class of the device. As mentioned before, the electromechanical meter can measure the real power only, while the microvip3 and the multimeter DMTME calculate the incoming reactive power without identifying whether it is inductive or capacitive with respect to the load specification. The last two instruments offer a wide variety of measurements for better information.

For ease of identifying the values monitored, certain IDs were used, including

• A+: stands for real power (Module Value 1.8.0);
• Q+ is identified as reactive power through an inductive load (Module Value 3.8.0);
• Q− is established as reactive power through a capacitive load (Module Value 4.8.0).

The MM200 and the smart MK10A have similar results, with the EDMI model showing slightly higher values, as smart devices have more accuracy compared to old electromechanical meters [15]. Zennio has less accuracy, but that is considered as typical, since it is not a typical smart meter, such as the widely used Mk10A. Similarly, the Microvip and Dmtme modules are validation tools used mainly for monitoring purposes; hence, they are not appraised as meters, as their precision in constant metering is lower, as shown in Figure 10.

Measurements for real power, which is available for all meters, are summarized in

Table 2. Total real power consumption (A+) in kilowatt-hours for each meter.

A/A	Landis&Gyr MM2000	EDMI Mk10A	ELControl Microvip3	ABB DMTME	Zennio KES-Plus
1	0	0	0	0	0
2	70.60 *	71.00	45.00	60.20	69.00
3	206.70	208.50	123.50	177.20	208
4	371.40	364.60	273.00	312.70	355.00
5	507.20	511.95	382.80	436.30	497.00
6	680.90	684.90	534.80	580.50	663.00
7	821.30	828.00	623.20	687.70	798.00
8	908.30	914.10	702.00	755.20	887.00
9	953.90	960.00	742.30	792.20	914.00
10	1000.40	1006.75	783.25	830.10	959.00
11	1071.70	1081.40	838.10	888.60	1032.00
12	1150.20	1157.30	954.45	953.70	1106.00
13	1229.70	1237.40	1057.20	1020.00	1184.00
14	1280.80	1288.70	1104.10	1061.30	1233.00
15	1352.80	1360.90	1206.70	1122.00	1304.00
16	1401.40	1409.75	1247.20	1160.70	1351.00

* All values are in KWh.

.

In the next step, positive reactive power, caused by an inductive motor, is measured. The electromechanical meter cannot measure this type of power, so values are not available. The other four modules can efficiently compute the inductive reactive power, with the Mk10A, Microvip 3 and Zennio appearing to have almost identical values, with small fluctuations, which are depicted in Figure 11.

The ABB model is presenting increased accuracy and complies with the specifications of the manufacturer [25]; thus, it is ideal for industrial use, where precision in measuring reactive power is essential, as it can save costs for both the consumer and the energy supplier. All values for the Q+ reactive power are outlined in

Table 3. Total positive reactive power consumption (Q+) in KVAr for the 4 modules.

A/A	Landis&Gyr MM2000	EDMI Mk10A	ELControl Microvip3	ABB DMTME	Zennio KES-Plus
1	N/A *	0	0	0	0
2	N/A	1.5	1.1	1	1
3	N/A	3.2	2.8	2.7	4
4	N/A	6.5	6.3	7.9	7
5	N/A	9.7	9.2	11.2	9
6	N/A	16.1	16.3	18.5	13
7	N/A	17.9	18.1	24.4	17
8	N/A	19.3	19.6	26.4	19
9	N/A	19.4	19.8	27.1	20
10	N/A	19.6	20.1	27.8	21
11	N/A	20.1	20.7	30	23
12	N/A	21.4	24	35.3	24
13	N/A	22	24.8	36.4	25
14	N/A	22.5	25.4	37	26
15	N/A	23.5	27.3	40.3	27
16	N/A	24.3	28.7	41.7	28

* Values are not available as not measurable by the module

.

In this section, the computation of negative reactive power, or capacitive power, is presented. The only meters capable of measuring this value are the classic Zennio monitoring and automation tool, along with the Mk10A smart meter, as shown in Figure 12. This capability is one of the reasons the EDMI model is widely applied to the Greek distribution system as the main metering device [20,28]. Regarding accuracy, Zennio KES-Plus is more precise as a monitoring tool, but the variation is negligible at 12 kVAr after testing. Consequently, the EDMI module appears to have the best performance, as shown in

Table 4. Total negative reactive power consumption (Q−) in KVAr for the metering arrays.

A/A	MM2000	EDMI Mk10A	Microvip3	ABB	KES-Plus
1	N/A *	0	N/A	N/A	0
2	N/A	50	N/A	N/A	43
3	N/A	144	N/A	N/A	139
4	N/A	240	N/A	N/A	239
5	N/A	327	N/A	N/A	326
6	N/A	428	N/A	N/A	431
7	N/A	504	N/A	N/A	510
8	N/A	553	N/A	N/A	560
9	N/A	581	N/A	N/A	585
10	N/A	610	N/A	N/A	615
11	N/A	656	N/A	N/A	660
12	N/A	684	N/A	N/A	700
13	N/A	720	N/A	N/A	730
14	N/A	748	N/A	N/A	760
15	N/A	777	N/A	N/A	790
16	N/A	808	N/A	N/A	820

* Values are not available for the 3 meters

.

For the final step, the two main units applied in the Greek distribution systems are tested and directly compared and illustrated in Figure 13. As mentioned previously, the EDMI Mk10A has the best mean accuracy, and it is the most affordable and practical device to install as an energy meter; hence, it is widely adopted in energy metering applications.

As it can be seen, all values have a normal increasing trend, so testing was conducted as expected, without any faults. Even though a typical meter measures only the real power in a commercial building, reactive power caused by inductive or capacitive loads is sufficient and should be computed as well, since it causes voltage drops, excessive heat and power losses, as shown in Figure 14 [20,29]. Hence, the adoption of smart meters will let the supplier know the kind of power they provide, adjust pricing according to load forecasting [16] and, most importantly, prevent electricity theft.

A smart meter can allow for an additional setting, now unavailable to most customers: the utilization of electric vehicles. As power management strategies can now be applied by the user even from a smartphone application, load demand is decreased; hence, the EV can be charged sufficiently, without power drops affecting the building energy requirements [13,14,15,16,17,18,19,20]. When the consumer demands more power for home appliances, e.g., air-conditioning during peak hours, the smart meter reduces the power output of the EV charger to avoid overload, while still providing adequate energy, in a completely automated sequence.

The typical procedure for smart meter integration and the replacement of the bulky and old electromechanical module requires minimum infrastructural changes [30]. The old meter is removed, and only the case with the wiring remains, which is identical. Then, the new meter is installed, as the wiring is deployed as shown in Figure 5. The last step is based on the registration of the smart meter for the specific household via an online application to establish 4G communication, so that online monitoring and operation become available. Hence, additional equipment is not needed, while the process requires about 30 min for a moderately experienced technician. However, more time may be required if the meter is located in the basement, as an additional antenna can be installed for 4G signal amplification.

The results of this layout present a variety of advantages introduced by the adoption of smart meters, including

• High efficiency and reliability;
• Multi-metering ability for different values;
• Pricing configuration;
• Real-time data transmission and storage;
• Wired or wireless connection;
• Power quality enhancement;
• Compatibility with smart home systems;
• Gradual prevention of electricity theft;
• Easier and faster fault detection;
• Remote load control.

It is suggested that these benefits outweigh any potential drawbacks, such as data privacy and radiation, which have to be studied further. Data privacy is a particularly easy task to address with modern cloud storage; however, any potential damage from irradiation should be evaluated to assess the significance of its effects, as cellular communication can be harmful for the user.

Usefulness, ease of use and remote control are key factors for the consumers’ acceptance and adoption of a smart meter [19]. Energy savings and adaptability to the smart grid system also play a significant role in in achieving that goal. However, certain users do not comply with this rule, as there are concerns about data privacy and radiation through the mobile communications network that smart meters utilize, affecting the behavior of the consumers [20]. This matter needs to be addressed, reinforcing the transition to smart metering and the era of smart grid applications.

Data privacy and radiation exposure concerns can be mitigated through an integrated approach that incorporates advanced technology, robust regulatory compliance and consumer engagement. First of all, in the case of data privacy, smart meters need to use end-to-end encryption where data are transmitted and stored. Modern encryption standards, such as AES-256, should be updated regularly [31]. The data collection should be based on the principle of minimization, where only that amount of data necessary for billing, operational efficiency and regulatory compliance are gathered, while anonymizing or aggregating them whenever possible. Access to the system should be granted only by strong authentication protocols such as multi-factor authentication and device-specific digital certificates. Periodic security audits, including third-party penetration testing, are required to identify and address vulnerabilities [32].

Transparency is a key feature to consumer trust, and utilities should clearly specify how the data are collected, used and shared, offering user-friendly dashboards to enable customers to easily monitor their own data. Additionally, consumers should have options to opt out of non-essential data collection or advanced features, ensuring a privacy-by-design approach. Compliance with data protection regulations, such as GDPR or CCPA, is non-negotiable, and utilities must maintain detailed records and audit trails to demonstrate adherence [33].

Low-power communication protocols such as Zigbee, LoRa, NB-IoT (NarrowBand IoT) and LTE-M, should be adopted to reduce radiation exposure. Dynamic power control is also recommended to adjust the transmission power according to the network distance [34]. This can be achieved by duty cycling, in order to reduce the transmission frequency to only when needed. In addition, the meters installed, based on placement guidelines, minimize human exposure to EMFs by being installed on exterior walls or away from high-occupancy areas. Regular testing for EMF needs to be performed to assure compliance with the safety standards set forth by organizations such as the International Commission on Non-Ionizing Radiation Protection. Consumers should be provided with transparent information regarding the level of radiation and safety certification to allay their fears. This needs to be supported by clear technical reports that could explain the potential risks [35]. Utilities must comply with the limits on radiation exposure set by both local and international authorities and work proactively with regulators to make sure the standards are evidence-based. Building consumer trust requires educating customers about the benefits, risks and safeguards of smart meters through well-designed educational campaigns.

Similarly, utilities also have to engage stakeholders by working with consumer advocacy groups, regulators and health experts in order to develop best practices and address public concerns through forums or feedback mechanisms. Independent certification of privacy and safety enhances credibility, and these certifications should be prominently displayed [36]. They should also provide consumers with access to tools for viewing and managing their data, modify device settings, and set data-sharing preferences. A very clear plan, with a sound response to incidents, needs to be put in place to address data breaches or health concerns quickly and transparently. By embedding these measures into their operations, utilities can balance innovation with safety and trust, ensuring regulatory compliance and long-term consumer acceptance.

Regarding electricity thefts, there are certain strategies that can be applied in smart meters [10]. The utilization of the Advanced Metering Infrastructure (AMI) enables real-time monitoring of energy consumption at granular intervals. Unlike electromechanical meters, which rely on periodic manual readings, smart meters continuously transmit usage data to utility providers. This constant flow of data facilitates the detection of anomalies that may indicate energy theft, such as

• Irregular consumption patterns;
• Alarms for intervention on the meter case, router or wiring cover.

Another exclusive and interesting feature is the use of comparative analytics, utilizing machine learning algorithms, where smart meters aggregate data from multiple meters within a distribution network to identify discrepancies between the total supplied energy and the cumulative energy recorded at consumer endpoints [37]. Large discrepancies may indicate theft at a specific point within the network via clustering and anomaly detection techniques. Smart meters also incorporate tamper detection mechanisms, such as magnetic field sensors, which detect physical interference, and event logs, which record power outages, reverse energy flow or sudden connection loss. These features provide real-time alerts to utilities, enabling rapid investigation.

In contrast to traditional electromechanical meters, which rely heavily on physical inspection and manual analysis to detect theft, smart meters leverage automation and advanced analytics to provide more precise, scalable and timely solutions. This shift significantly reduces detection time, increases the accuracy of theft identification and minimizes revenue losses for utility providers. The integration of these technologies represents a substantial improvement over conventional theft prevention methods.

In addition, smart meters enable dynamic pricing models that have big implications on consumer behavior, as well as policymaking and utility management. These models consist of time-of-use pricing, critical peak pricing, and real-time pricing, encouraging consumers to shift energy consumption away from peak demand periods by setting varying electricity rates based on grid conditions [38]. Smart meters provide detailed feedback on energy use patterns and real-time price signals that would reward consumers for being more energy efficient, lower overall consumption and better match their demand to periods of either low demand or high renewable energy supply.

Similarly, policy makers can promote smart meter deployment and dynamic pricing programs to help enhance grid reliability, reduce greenhouse gas emissions and optimize energy resource allocation. Utilizing the electricity demand–response model, the loads are divided into two types: shiftable and interruptible. Therefore, the policy makers know which load can be time-shifted to enhance grid stability [39]. By enabling more responsive demand-side management, smart meters support the integration of renewable energy sources and help maintain grid stability to raise and promote energy awareness and accountability.

Flexible dynamic pricing models will grant the utility operator several operational and financial benefits. Load forecasting is now more accurate with reduced peak load demand, while infrastructural upgrades could be postponed, since the demand curve is flattened [40]. Utilities can make use of smart metering to promote better pricing strategies through real-time data, improving the customer satisfaction index and minimizing energy theft.

Hence, smart meters are the future of metering systems. They should indeed be applied on a large scale for both commercial and industrial use in the coming years, as highlighted in the literature [20,21,22,23,24,25]. The findings of this paper and the comparison of the five modules are summarized in

Table 5. Summarization–comparison of the meters’ characteristics [24,25,26,27,28,29].

A/A	Module	Advantages	Disadvantages
1	Landis&Gyr MM2000	Low costs Long presence in the market High accuracy	Old technology Bulky and heavy Vulnerable to electricity theft Lack of reactive power monitoring
2	EDMI Mk10A	High reliability and accuracy Remote monitoring Hard to breach Dynamic pricing application Multi-parameter metering	Risk of electromagnetic interference Priced higher than the MM2000 Remote operation shut down Data privacy concerns
3	ELControl Microvip3	High precision and reliability Great validation tool Low interference	Not a typical meter Costly and heavy Poorly sized Incompatible with Remote Metering Network
4	ABB DMTME	Good accuracy Compact utilization as a monitoring tool, High metering abilities	Mainly a validation tool for large-scale applications High costs Bulky and heavy Complex operation
5	Zennio KES-Plus	Great metering abilities, e.g., reactive power Compatible with the KNX protocol Great potential and high precision Compact and inexpensive	Priced higher than the Mk10A Complex wiring and installation Requires additional components Exposed to interference.

.

Smart meters can be easily integrated into smart grid networks. Advanced data analytics tools and decision-making systems will be responsible for processing the large inflow of data coming from the smart meters and other components of the grid. This would enable utilities to identify trends, detect patterns, and make informed recommendations for optimization of electricity flow. Integration with sophisticated communication protocols ensures seamless interaction between various systems within a smart grid, enabling efficient management and operational harmony [5].

Data analysis tools, such as machine learning algorithms and statistical modelling frameworks, are crucial for processing the collected data. These tools analyze historical and real-time consumption patterns, weather forecasts and grid performance metrics to predict demand trends and identify potential issues, such as grid overloads or outages [3,4,5,6,7]. Techniques such as clustering and regression are commonly employed to segment consumer behavior, while neural networks and time-series analysis help forecast energy demand with high accuracy. By identifying consumption anomalies or inefficiencies, these tools support proactive maintenance, fraud detection and energy conservation initiatives.

Decision-making tools, acting on the output from data analytics, provide recommendations on actionable strategies to help in grid optimization. Tools such as optimization algorithms and multi-agent systems consider different scenarios for the most effective utilization of energy resources. These are helping utilities in dynamic pricing model implementations, the optimization of load balancing and the integration of renewable sources. This is achieved through analyzing their intermittency and generating forecasting [6]. Advanced tools will also have the capability to enable distributed energy resource management to make technologies such as battery storage, electric vehicles and solar panels seamlessly integrated.

IEC 61850, IEEE 2030.5 and MQTT are some of the communication protocols which will play a crucial role in interfacing these analytics systems with other components of the smart grid [41,42]. These protocols ensure interoperability and real-time information exchange among grid operators, distributed energy resources and consumer systems. Utilities can be enabled for dispatching energy, manage grid stability, and provide real-time feedback to consumers on energy usage and pricing through bidirectional communication.

The key capabilities these utilities have, including data analytics tools, decision-making frameworks and communication protocols, all ensure optimized flow of electricity, make the grid resilient and enhance operational efficiencies for smart grids to meet the demanding needs of modern energy systems, making electricity distribution sustainable and reliable.

IEC 61850 is an international standard designed for substation automation and smart grid applications. It enables interoperability between devices and supports real-time communications, essential for grid stability. Its features include high-speed data exchange through protocols such as GOOSE (Generic Object-Oriented Substation Event), scalable architecture suitable for large-scale deployments, and support for Advanced Metering Infrastructure (AMI) integration. IEC 61850-enabled intelligent electronic devices (IEDs) can be deployed in substations to facilitate seamless data exchange and using logical nodes for metering, control and protection functions, ensuring compatibility with existing grid elements through SCADA [42]. Τhe central server reacts to the invoked message with detailed response of the executed command such as a power switch operation, and informs the SCADA user.

MQTT (Message Queue Telemetry Transport) is a lightweight, publish–subscribe protocol designed for low-bandwidth and high-latency networks and the most renowned amongst Machine to Machine communication protocols. It is ideal for IoT applications, including smart meters, through low network overhead for secure communication and scalability using TLS/SSL certificates. A study [43] implemented MQTT brokers to manage communication between smart meters and the utility back-end, integrating MQTT with data analytics platforms to process real-time meter data. Hierarchy and signal priority are the main advantages of this protocol, where only users with the required access can read the broker’s messages, ensuring confidentiality and data protection [44].

Zigbee is a low-power wireless protocol based on the IEEE 802.15.4 standard. It is commonly used in home automation and smart energy systems due its ease of installation, reliability, low cost and safety feature [45]. It supports mesh networking for reliable communication in dense urban environments where wireless communications are difficult to propagate, providing a highly user friendly interface. Another study [46] proved that Zigbee-enabled meters can be employed locally at the central panel to generate invoices and monitor continuous electricity usage with 99.8% accuracy. Computed data are then transmitted and stored to a database with online access. Hence, users can understand how their needs affect the power profile in real-time to reduce energy use, as they are better informed and motivated to do so. In addition, the energy consumption behavior of a user can be modified to encourage more energy-saving habits, such as avoiding leaving the lights or the heating on if it not required.

LoRa is a long-range, low-power protocol suited for rural and remote smart metering applications. Its features include a wide coverage area with minimal infrastructure and high penetration in dense environments [47]. A study [48] implemented a LoRa module using the LoRa-WAN protocol to monitor the temperature and electricity utilization of the household central heating system. The results showed that data losses were minimized within a 1 km radius, while the module consumed barely any power, implementing mobile access and overcoming the disadvantages; data loss and low security, of 4G router currently employed. Hence, the user only utilizes heat when it is really required, so energy waste is reduced.

Formerly known as the Smart Energy Profile (SEP), the IEEE 2030.5 protocol supports distributed energy resources (DERs) and demand–response programs. The dynamic response scheme deploys the energy profile of each user through data encryption models, for maximum safety, and utilizes an optimization technique for load shifting, leading to a reduction in peak demand by 22% [49]. The protocol features include enabling two-way communication between utilities and DERs and securing data exchange using HTTPS and TLS. IEEE 2030.5 can be integrated with DER management systems (DERMS) cloud servers as all messages are transcribed to optimize DER participation. This layout can be applied to V2G systems where the smart meters communicates with the bidirectional converter that the EV is plugged into. Then, SEP is employed to monitor the power exchange between the EV and the household, ensuring energy sufficiency [50].

To summarize, protocols such as IEC 61850 ensure high-speed, real-time data transfer, enabling accurate monitoring and control. Standards such as IEC 61850 and IEEE 2030.5 facilitate seamless integration of smart meters with other grid components, reducing operational silos. Protocols such as MQTT and LoRa enable scalable smart meter networks, adaptable to both urban and rural environments. Advanced encryption methods in MQTT and IEEE 2030.5 safeguard sensitive data, ensuring compliance with regulatory standards. Wireless protocols such as Zigbee and LoRa minimize infrastructure costs, particularly in areas with limited connectivity. Future efforts may focus on the development of hybrid communication models combining multiple protocols for optimized performance, employing AI-driven analytics to process data collected via smart meters.

4. Conclusions

This study provides an in-depth analysis of energy management through the integration of smart meters, highlighting their transformative role in modernizing the energy landscape. The experimental comparison between traditional electromechanical meters and advanced smart meters revealed several critical advantages of the latter. Smart meters demonstrated superior accuracy in real-time energy consumption monitoring, practically identical to renowned modern analyzers, and integration into smart grids. These attributes enhance power system reliability, minimize losses and support dynamic pricing models that encourage off-peak energy use.

Additionally, smart meters were shown to significantly reduce operational inefficiencies, including energy theft and manual labor requirements. The EDMI Mk10A, a widely adopted smart meter in Greece, stood out for its precision and practicality, both in real and reactive power monitoring, hence cementing its role as a key factor of the national distribution system. The KesPlus Module by Zennio induces great characteristics; however, it is still more complex and has higher costs compared to the EDMI model. However, this study also identified barriers to widespread implementation, such as high initial costs, the need for infrastructure upgrades and consumer concerns over data privacy and electromagnetic exposure. These issues must be addressed through robust cybersecurity frameworks, transparent communication strategies and public awareness campaigns.

To address data privacy, smart meters should use end-to-end encryption, data minimization, and robust authentication protocols like multi-factor authentication and digital certificates, while adhering to regulations such as GDPR or CCPA. Regular security audits and transparency about data collection and usage foster trust, with consumers given options to monitor, manage or opt out of non-essential data collection. For radiation exposure, low-power communication protocols such as Zigbee or LoRa, dynamic power control, and limited transmission frequency through duty cycling can minimize risks. Placement guidelines should ensure that the meters are installed away from high-occupancy areas, with regular EMF testing to confirm compliance with standards such as those from ICNIRP. Consumer education campaigns, third-party safety certifications, and tools for data management build trust, supported by a robust incident response plan for addressing breaches or concerns.

Key communication protocols such as IEC 61850, IEEE 2030.5 and MQTT enable theseamless integration of analytics systems within smart grids by ensuring interoperability and real-time information exchange among grid operators, distributed energy resources, and consumers. These protocols support bidirectional communication, allowing utilities to dispatch energy, maintain grid stability and provide real-time feedback on usage and pricing. Combined with advanced data analytics tools and decision-making frameworks, they enhance electricity flow optimization, grid resilience and operational efficiency, ensuring sustainable and reliable energy distribution for modern systems.

In conclusion, the adoption of this technology represents a vital step toward achieving energy efficiency and decarbonization targets, as their benefits far outweigh the limitations, underscoring the need for accelerated deployment.

Future work should be directed to the development and implementation of an all-rounded set of metrics for performance evaluation of the five meters discussed in this study by using metrics such as mean absolute error or root mean square error. Other investigations will consider the reliability and robustness of the meters, checking failure rates and operational uptime under harsh environmental conditions regarding temperature, moisture and electromagnetic interference. The efficiency of the data transmission or communication capabilities is to be checked mainly about transmission latency, packet loss, and bandwidth utilization at the time of operation. In addition, adaptability from the meters is to be tested regarding dynamic pricing models, which, by integration into smart grid systems, should achieve fast response times in terms of speed and synchronization. In addition, it is necessary to focus on enhancing data protection measures, validating the health impacts of wireless communication technologies, and exploring cost-effective strategies for large-scale implementation. By addressing these challenges, smart meters can play a pivotal role in creating a sustainable and resilient energy ecosystem.