Abstract
As the global demand for sustainable energy solutions intensifies, industries are under mounting pressure to monitor and manage their energy consumption with greater precision. Traditional energy metering methods—centered on fixed 15-minute intervals—struggle to deliver the real-time insights necessary to achieve cost efficiency and meet regulatory targets. Enter the transformative power of Artificial Intelligence (AI) and the Internet of Things (IoT).
Together, AI and IoT are revolutionizing energy monitoring systems by enabling high-frequency, data-rich measurements, real-time analysis, predictive demand forecasting, and intelligent automation. These technologies allow industries to not only visualize their energy profiles with unprecedented granularity but also to optimize usage dynamically, reduce peak demand charges, and contribute to global sustainability goals.
This review article explores the intersection of AI and IoT within the context of industrial and utility energy management. We highlight the current limitations of traditional metering, examine the core capabilities of AI and IoT in delivering precision monitoring, and showcase real-world applications—including innovations introduced by companies like Benetoos. The synergy between IoT devices and AI algorithms marks a new frontier in energy intelligence—one that promises transparency, fairness, and operational excellence in energy consumption.
- Introduction
In recent decades, the rapid growth of industrialization, urbanization, and digital infrastructure has dramatically increased global energy consumption. According to the International Energy Agency (IEA), industrial energy use alone accounts for nearly 37% of total global energy demand, with projections indicating continuous growth. This surge in demand has intensified the need for smarter, more precise ways to monitor and manage energy across industries.
Traditional energy monitoring systems have remained largely static. Most rely on fixed-time intervals—typically 15-minute blocks—to measure and calculate energy consumption. While this model has served utilities for decades, it has critical shortcomings in the context of dynamic, high-load environments. In sectors such as steel manufacturing, cement production, mining, and renewable power generation, energy consumption patterns fluctuate rapidly and unpredictably. Capturing these fluctuations using a rigid, interval-based system often results in inaccurate peak demand measurements, leading to unfair billing, operational inefficiencies, and missed opportunities for optimization.
The world now finds itself at the brink of an energy intelligence revolution. Two forces—the Internet of Things (IoT) and Artificial Intelligence (AI)—are converging to radically redefine how energy data is collected, analyzed, and acted upon. IoT devices such as smart meters, sensors, and edge processors are making it possible to gather real-time data from every corner of an industrial operation. At the same time, AI technologies—including machine learning, neural networks, and predictive analytics—are unlocking the ability to interpret that data instantly, draw actionable insights, and automate responses.
This article aims to present a comprehensive review of the role of AI and IoT in advancing precision energy monitoring. We will examine the key technologies driving this transformation, analyze the shortcomings of conventional systems, and explore practical applications in both industrial and utility-scale settings. Special attention will be given to real-world innovations, including Benetoos’ unique approach to synchronized, multi-line metering with rolling demand windows—demonstrating how cutting-edge solutions can deliver both technical precision and economic value.
- Fundamentals of IoT and AI in Energy Systems
2.1 Understanding the Internet of Things (IoT) in Energy Monitoring
The Internet of Things (IoT) refers to a network of interconnected devices that collect, exchange, and process data in real time. In the context of energy monitoring, IoT enables a shift from periodic data collection to continuous, high-resolution insights—a fundamental leap for both industrial and utility-scale applications.
At its core, an IoT system in energy monitoring includes the following components:
• Smart Meters and Sensors: Devices that capture granular data on voltage, current, power factor, frequency, and energy consumption at intervals as short as one second—or even 10 seconds as in Benetoos’ system.
• Edge Devices and Gateways: Intermediate devices that preprocess data and transmit it to centralized or cloud-based servers.
• Connectivity Infrastructure: Communication protocols such as RS485, TCP/IP, Wi-Fi, NB-IoT, or fiber optics ensure secure and rapid transmission of energy data.
• Data Platforms: These receive, store, and structure energy usage data from numerous sources, enabling visualization and reporting.
One of the key innovations enabled by IoT is remote energy monitoring. Unlike legacy systems that require manual meter readings and physical inspections, IoT networks empower utilities and industries to track real-time performance across substations, feeder lines, and distributed generation (DG) units. In some cases—like Benetoos’ border projects—IoT-enabled systems even facilitate cross-border energy billing through synchronized data collection, SIM-based modems, and encrypted communication.
IoT’s true value lies not just in data collection but in contextual awareness. When embedded in a feedback loop that includes AI-driven analytics, IoT systems can predict anomalies, trigger alerts, and initiate corrective actions autonomously—bringing intelligence to what was once a passive measurement system.
2.2 Basics of Artificial Intelligence (AI) in Energy Systems
While IoT devices capture and transmit raw data, Artificial Intelligence (AI) is what turns that data into actionable insights. At its core, AI in energy monitoring involves the use of algorithms that can learn from patterns, recognize anomalies, and make decisions with minimal human input.
Key AI techniques used in energy systems include:
• Machine Learning (ML): Algorithms trained on historical energy data to detect patterns, forecast demand, and identify operational inefficiencies.
• Deep Learning (DL): Advanced neural networks that analyze multi-dimensional energy signals for more complex tasks, such as real-time classification of energy loads or fault detection.
• Predictive Analytics: Forecasts future energy usage and potential peak demand based on current and past behavior.
• Reinforcement Learning: Systems that learn optimal energy usage strategies through feedback loops, often used in smart grid management.
In the realm of precision energy monitoring, AI performs tasks that are virtually impossible through manual methods or rule-based systems alone. For example, it can:
• Identify abnormal consumption patterns that may indicate energy theft, equipment malfunctions, or inefficiencies.
• Predict demand spikes using weather data, production schedules, and historical load curves—allowing industries to preemptively shift loads or adjust operations.
• Optimize billing algorithms based on dynamic energy tariffs and real-time consumption patterns.
Moreover, when AI is paired with rolling window measurement strategies, as seen in Benetoos’ system, the combination allows for adaptive calculation of peak demand—significantly improving fairness and accuracy in high-load environments. This approach ensures that peak billing is based on actual simultaneous consumption across multiple feeders or lines, rather than fragmented or averaged data.
Together, IoT and AI don’t just automate energy monitoring—they transform it into a real-time, intelligent, and predictive system that supports both economic and environmental goals.
- Current Challenges in Traditional Energy Monitoring
Despite decades of refinement, traditional energy monitoring systems—particularly those based on static interval metering—are increasingly insufficient to meet the demands of modern industries. These conventional systems typically use fixed 15-minute intervals to measure and record energy usage, and while they have long served as the industry standard, they now reveal critical gaps when examined under the lens of precision, efficiency, and adaptability.
3.1 Limited Granularity and Delayed Data
One of the most significant drawbacks of traditional systems is their inability to capture real-time energy fluctuations. In high-consumption sectors such as mining, steel, cement, and copper production, energy use can vary dramatically in just seconds. Fixed-interval meters fail to capture these momentary peaks, which can lead to:
• Underestimation or overestimation of peak demand, resulting in billing inaccuracies.
• Inability to identify transient anomalies or operational inefficiencies.
• Delayed response to energy events, reducing opportunities for dynamic load shifting or optimization.
3.2 Isolated Metering and Manual Aggregation
Another major limitation is the lack of synchronization across multiple feeders or transmission lines. In large-scale facilities, power is often drawn from multiple sources simultaneously. Traditional systems measure each line separately and often require manual aggregation of readings—a process prone to errors and delays.
For example, a cement plant might use three feeders. If each is metered individually without real-time synchronization, the peak demand recorded may not represent the plant’s actual cumulative maximum, leading to:
• Overbilling based on disjointed peaks, not simultaneous usage.
• Missed insights into coordinated energy behavior across systems.
This fragmentation limits the visibility industries need to manage energy strategically.
3.3 Inflexibility for Modern Energy Ecosystems
Traditional meters were not designed for today’s distributed energy environments. As industries begin incorporating renewable energy sources, microgrids, and distributed generation (DG) into their operations, legacy systems struggle to:
• Integrate data from diverse energy sources.
• Adjust dynamically to bidirectional power flows.
• Support real-time analytics or predictive modeling.
Moreover, with rising energy costs and evolving regulatory pressures toward net-zero emissions, industries demand greater transparency, accuracy, and accountability in energy data—standards that fixed-interval meters simply cannot meet.
3.4 Case Studies: Real-World Failures of Traditional Monitoring
Numerous industrial facilities have already experienced the shortfalls of conventional monitoring. In Iran, for instance, industries using traditional metering faced issues such as:
• Incorrect peak demand charges due to asynchronous data collection.
• Manual intervention to interpret energy behavior, leading to inefficiencies.
• Incompatibility with remote reading or automation systems, requiring expensive upgrades or workarounds.
Companies like Benetoos have encountered these gaps directly in their fieldwork. By repairing and retrofitting hundreds of ACTARIS, EDMI, and ISKRA meters across substations and industrial plants, they’ve consistently found issues like:
• RS485 communication failures,
• Meter sync problems,
• Repeated demand calculation errors due to poor coordination across lines.
These challenges underscore the urgent need for a new paradigm in energy monitoring—one that embraces intelligence, connectivity, and automation at its core.
- The Synergistic Impact of AI and IoT on Energy Monitoring
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is redefining the capabilities of energy monitoring systems. When used in isolation, IoT enables data collection, while AI brings interpretation and decision-making. But when combined, these technologies create a powerful ecosystem that not only monitors energy consumption with unprecedented accuracy but also optimizes and automates it in real-time.
4.1 Enhanced Accuracy Through Real-Time Data Collection
Traditional meters are often restricted to sampling data at 15-minute intervals, which is insufficient for industrial environments with highly variable loads. With IoT, it becomes possible to collect data at intervals as short as every 10 seconds, ensuring a continuous stream of precise energy readings.
In systems like those developed by Benetoos, IoT-enabled smart meters on multiple transmission lines are connected to data concentrators and synchronized using advanced communication protocols such as RS485, TCP/IP, or fiber optics. This allows:
• Real-time monitoring of energy consumption across different feeders or sources.
• Simultaneous capture of energy peaks from multiple lines.
• Higher resolution data sets that serve as the foundation for AI analytics.
This level of data granularity enables organizations to respond instantly to usage changes, detect inefficiencies, and plan operations more strategically.
4.2 AI-Driven Data Analysis and Optimization
Once IoT devices have gathered real-time energy data, AI steps in to extract value from it. With machine learning and pattern recognition algorithms, AI systems can analyze complex datasets, detect anomalies, and predict future consumption trends.
Some of the most impactful applications of AI in precision energy monitoring include:
• Demand Forecasting: AI can predict upcoming energy needs based on historical data, environmental factors, and operational schedules. This allows industries to pre-load shift, reduce demand charges, and avoid penalties.
• Anomaly Detection: AI models can detect irregular patterns in voltage, current, or power factor—flagging potential faults or inefficiencies before they escalate.
• Optimization Algorithms: AI enables dynamic load balancing and power flow optimization to reduce energy waste and improve overall efficiency.
In the Benetoos model, for example, AI is applied to aggregate data from multiple feeders, apply a rolling 10-second demand calculation, and identify true peak demand points that better reflect actual industrial usage. This not only improves billing fairness but also supports grid stability and cost savings.
4.3 Scalability and Integration Capabilities
One of the often-overlooked benefits of combining AI and IoT is scalability. These systems can easily expand across large industrial plants, utility substations, or even multiple geographic locations. IoT’s modular architecture means that new sensors and meters can be added without redesigning the whole system, while AI models can be retrained or fine-tuned to handle new data sources and energy behaviors.
Additionally, integrated platforms allow for seamless interoperability between different hardware vendors and data protocols. This ensures that organizations are not locked into one ecosystem and can gradually upgrade their infrastructure without full replacement—an approach Benetoos actively promotes by retrofitting existing IEC 62056-compatible meters rather than demanding costly new devices.
This flexibility is crucial in markets like Canada, the Middle East, and emerging economies, where energy infrastructures are evolving rapidly and require adaptable, cost-effective solutions.
- Applications of AI and IoT in Precision Energy Monitoring
While the technical capabilities of AI and IoT are remarkable, their true value emerges in real-world applications—where energy accuracy, operational control, and billing fairness have a direct impact on cost savings and sustainability. In this section, we explore how these technologies are being deployed in three core areas: industrial energy management, smart grids and renewables, and cross-border energy trade.
5.1 Industrial Energy Management
Industries like steel, cement, mining, and copper processing are among the most energy-intensive sectors in the world. Small fluctuations in energy pricing, consumption, or billing errors can lead to significant financial losses. Traditional monitoring systems often fail to address these high-resolution demands—opening the door for AI+IoT solutions.
✅ Real-Time Load Tracking and Peak Demand Control
Smart meters installed across different feeders are synchronized via IoT and analyzed in real time using AI-driven demand algorithms. For instance, Benetoos’ rolling 10-second demand measurement method allows plants to identify actual peaks based on aggregated and synchronized multi-line data—rather than fragmented per-line peaks.
This innovation solves a critical problem: overbilling due to misaligned demand measurement. By offering a comprehensive view of real-time usage, Benetoos empowers energy managers to adjust operations preemptively, reducing maximum demand penalties.
✅ Remote Monitoring and Fault Detection
In complex industrial setups, quick detection of failures—such as RS485 port malfunctions or energy surges—is essential. With IoT-enabled meters and AI diagnostics, such anomalies are detected instantly, and alerts are issued to the control center. This reduces downtime, prevents equipment damage, and optimizes preventive maintenance cycles.
✅ Case Example
In several large-scale retrofitting projects, Benetoos refurbished over 500 industrial meters (e.g., ACTARIS SL7000 and EDMI MK6E), restoring communication functionality and integrating them into remote-reading systems. This enhanced visibility enabled industrial clients to monitor consumption hourly, respond to power quality issues, and optimize internal energy distribution.
5.2 Smart Grids and Renewable Energy Integration
The global shift toward renewable energy—solar, wind, hydro, and distributed generation (DG)—demands a new level of intelligence in the grid. Renewables introduce variability, bidirectional flow, and complex load balancing, which cannot be managed effectively with traditional metering.
✅ Grid Stabilization
AI models can forecast solar and wind generation based on weather data and usage trends. These forecasts help utilities balance supply and demand in real time. When coupled with IoT-based meters across substations and feeders, AI algorithms can shift loads, activate reserve capacity, or store excess energy with split-second timing.
✅ DG Site Monitoring
For solar farms and DG power plants, Benetoos’ systems provide remote data access, load profile visualization, and parameter logging every 30 seconds. These insights allow operators to fine-tune inverter outputs, detect outages, and maintain grid harmony.
✅ Microgrid Optimization
In self-contained microgrids (e.g., rural or off-grid sites), AI+IoT systems are essential for autonomous energy management. They balance consumption, storage, and generation without manual input—ensuring continuity, efficiency, and minimal waste.
5.3 Cross-Border Energy Trading
Energy trade between nations (such as Iran and Afghanistan) relies on precise metering, transparent billing, and secure data transfer. In one landmark project, Benetoos implemented a remote monitoring system at the zero point of Sarbisheh border, where energy exported to Afghanistan is measured via EDMI meters equipped with SIM-enabled modems.
✅ Key Functions:
• Automated data reading and transmission every 30 seconds.
• Remote access for both countries’ energy authorities.
• Fair and auditable billing based on accurate consumption logs.
Such solutions demonstrate how AI and IoT can enable trust, accuracy, and efficiency in international energy agreements.
- Technological Innovations and Emerging Trends
The field of precision energy monitoring is evolving rapidly, driven by the fusion of cutting-edge technologies. As industries embrace digital transformation, several emerging trends and technological innovations are reshaping how energy is measured, managed, and monetized. This section focuses on three of the most impactful trends: rolling demand windows, predictive analytics, and blockchain integration.
6.1 10-second Rolling Window: A New Standard?
One of the most disruptive innovations in energy measurement is the shift from fixed 15-minute demand intervals to 10-second rolling windows. Traditional meters capture the highest demand within each 15-minute window, often failing to reflect the true simultaneous demand across multiple feeders. This can result in inflated charges and inaccurate load assessments.
The Benetoos innovation introduces a summator module and specialized software that synchronize multiple meters and apply a rolling demand window across all data points. Key benefits include:
• Granular accuracy: Captures peak demand in near real time.
• Billing fairness: Prevents overcharging due to asynchronous readings.
• No hardware overhaul: Compatible with IEC 62056-21 meters, reducing upgrade costs.
This approach is particularly well-suited for large facilities using multiple transmission lines, as it offers synchronized peak detection across all lines simultaneously.
6.2 AI-Powered Predictive Analytics
Predictive analytics is shifting energy management from reactive to proactive. With historical consumption data, weather inputs, production schedules, and machine learning algorithms, AI can now forecast energy needs with increasing accuracy.
Key Applications:
• Load forecasting: Anticipating future demand peaks to reduce stress on the grid.
• Dynamic pricing: Adjusting tariffs based on real-time consumption and market conditions.
• Maintenance prediction: Using anomaly detection to schedule repairs before failures occur.
These AI-driven capabilities not only optimize energy use but also enable better decision-making and reduce operational costs. In competitive energy markets, this translates into a strategic advantage for both producers and consumers.
6.3 Blockchain Integration in IoT-based Energy Monitoring
As data integrity and trust become more important, blockchain technology is emerging as a powerful tool for securing energy transactions and metering records. Blockchain offers:
• Tamper-proof logs: Immutable records of energy consumption and transactions.
• Decentralized auditing: Real-time, transparent access for all stakeholders.
• Smart contracts: Automated billing, energy trading, and SLA enforcement.
In multi-party ecosystems—such as cross-border energy trade, community solar networks, or peer-to-peer energy sharing—blockchain ensures that data cannot be altered, and transactions remain trustworthy.
While adoption is still in early stages, the convergence of blockchain with AI and IoT is expected to redefine the future of energy data management, especially in markets where transparency and regulation are critical.
- Barriers and Challenges to Implementation
Despite their immense potential, the integration of AI and IoT in energy monitoring is not without its hurdles. From technical limitations and cybersecurity concerns to regulatory roadblocks and economic feasibility, organizations must navigate a range of challenges to adopt these technologies effectively. Understanding these barriers is essential for crafting realistic roadmaps and risk-mitigation strategies.
7.1 Technological and Infrastructural Barriers
One of the primary obstacles is the existing energy infrastructure, which in many industrial and utility environments is outdated and fragmented. Integrating modern IoT devices and AI platforms into these legacy systems often requires:
• Retrofitting old meters or replacing non-compliant hardware.
• Upgrading communication infrastructure (e.g., from RS232 to RS485 or TCP/IP).
• Ensuring interoperability between devices from different manufacturers.
Even when using flexible solutions like Benetoos’ IEC 62056-compatible modules, technical expertise and hardware coordination are essential to avoid data loss or misinterpretation.
7.2 Data Security and Privacy Concerns
As energy systems become increasingly digital, they also become more vulnerable to cyber threats. Real-time data streams, remote access capabilities, and centralized cloud platforms all introduce new attack surfaces for hackers.
Key concerns include:
• Data breaches that compromise consumption records and client identities.
• Tampering or spoofing of meter data, affecting billing accuracy and trust.
• Unauthorized control of energy systems, which can lead to sabotage or blackouts.
Securing IoT-based energy systems demands end-to-end encryption, multi-factor authentication, firewall protections, and continuous threat monitoring. AI systems themselves can be both targets and defense mechanisms in this context—detecting abnormal patterns indicative of cyberattacks.
7.3 Regulatory Compliance and Standardization
Regulatory environments often lag behind technological innovation. Many national and regional standards are still based on traditional metering protocols, which can limit the rollout of AI- and IoT-enhanced systems.
Challenges include:
• Unclear guidelines on the use of rolling window calculations for billing.
• Lack of certification frameworks for AI-based metering platforms.
• Varying compliance rules across countries, complicating cross-border energy trade.
For example, while Canada has progressive energy standards and incentives, many other countries still rely on fixed-interval systems and legacy reporting formats. Companies like Benetoos must balance innovation with compliance, often customizing their solutions to local frameworks.
7.4 Economic Barriers and ROI Uncertainty
Although long-term benefits of AI and IoT in energy monitoring are clear—reduced billing errors, optimized usage, and improved reliability—the upfront investment can be significant:
• Hardware procurement (smart meters, summators, routers).
• Software development and AI model training.
• Installation, calibration, and staff training.
• Ongoing maintenance and cloud service costs.
For some organizations, especially in developing regions or cash-strapped industrial sectors, the return on investment (ROI) may appear uncertain or too slow. Demonstrating short-term financial gains—such as reduction in maximum demand charges—can be key to encouraging adoption.
- The Roadmap to Adoption: Strategies and Recommendations
For industries and utilities seeking to modernize their energy monitoring infrastructure, success depends not just on selecting the right technology, but on building a structured, strategic approach. The adoption of AI and IoT solutions requires cross-functional planning, stakeholder alignment, and supportive policy frameworks. This section outlines key strategies to enable effective implementation at scale.
8.1 Industrial Strategies for Successful Implementation
Organizations should approach AI+IoT adoption in phased steps, ensuring minimal disruption and maximizing value:
🔹 Step 1: Assessment and Planning
• Audit current energy infrastructure, meter types, and communication systems.
• Identify pain points in current demand measurement and billing.
• Evaluate compatibility of existing meters (e.g., IEC 62056-21 compliance).
🔹 Step 2: Pilot Deployment
• Select one substation or plant section for pilot testing.
• Install smart meters, summators, and communication gateways.
• Monitor performance and data accuracy for 3–6 months.
🔹 Step 3: Integration with AI Platforms
• Feed pilot data into AI algorithms for predictive analysis and load profiling.
• Validate forecast accuracy and operational insights.
• Develop dashboards and automated alert systems for operators.
🔹 Step 4: Scale and Optimize
• Expand rollout across all facilities.
• Adjust algorithms based on use-case learning.
• Continuously review KPIs (peak demand reduction, downtime, billing accuracy).
Organizations like Benetoos have successfully followed this path in multiple projects—gradually scaling from meter refurbishments to full AI-integrated remote monitoring systems.
8.2 Government and Policy Recommendations
Governments and regulatory bodies play a crucial role in enabling technological advancement through clear policies and supportive infrastructure:
• Update metering standards to include AI-ready, rolling window-compatible systems.
• Subsidize hardware upgrades and cloud service costs for SMEs and industrial clusters.
• Mandate cybersecurity protocols for energy data transmission.
• Support local innovation by funding startups and research in smart energy.
In countries with clean energy goals—such as Canada’s net-zero by 2050 commitment—incentivizing AI+IoT systems can dramatically accelerate decarbonization and energy equity.
8.3 Strategic Partnerships and Ecosystem Collaboration
Adoption thrives in collaborative ecosystems. Utilities, industrial clients, technology vendors, and software providers must work together to ensure seamless interoperability and long-term support. Key recommendations include:
• Partnering with trusted integrators (e.g., Benetoos) for customized retrofitting and consulting.
• Engaging universities and R&D centers to co-develop AI models.
• Building joint ventures to share infrastructure and operational costs.
Cross-border energy projects also benefit from regional harmonization of standards, allowing seamless data sharing and billing reconciliation.
- Conclusion and Future Directions
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) into energy monitoring represents more than just a technological upgrade—it marks a foundational shift in how industries understand, manage, and optimize energy use. Together, these tools empower stakeholders with real-time visibility, predictive insights, and automated control—capabilities that were once unimaginable in legacy systems.
Through this review, we have highlighted:
• The inadequacies of traditional metering systems, including inflexible intervals and lack of real-time synchronization.
• The fundamental role of IoT in capturing granular, high-frequency energy data across distributed infrastructures.
• The transformative potential of AI in forecasting demand, detecting anomalies, and optimizing operations.
• The synergy between AI and IoT, as seen in innovations like Benetoos’ 10-second rolling window method for industrial peak demand accuracy.
• Real-world applications across heavy industry, smart grids, renewables, and cross-border electricity markets.
• The emerging technologies—from blockchain integration to predictive analytics—that will shape the next generation of energy monitoring.
• Key challenges, including cybersecurity, standardization, and economic barriers.
• Strategic roadmaps for industrial stakeholders, regulators, and technology providers.
As the world accelerates toward decarbonization, cost efficiency, and grid modernization, precision energy monitoring becomes not just a strategic asset—but a necessity. AI and IoT will increasingly underpin how energy is measured, reported, and optimized, enabling smarter decisions across the value chain.
Looking ahead, we foresee three major directions: - Deeper AI Integration: With the growth of edge computing and federated learning, AI models will operate closer to data sources, enabling even faster, localized decision-making.
- Global Standardization: Efforts will intensify to develop international standards for AI-based billing, rolling window calculations, and IoT security protocols.
- Democratization of Energy Intelligence: Scalable and affordable AI+IoT platforms will become accessible even to small businesses and developing markets—unlocking energy transparency for all.
In this unfolding frontier, companies like Benetoos are leading the charge—not just by developing smart tools, but by rethinking what smart energy really means.
Join the energy revolution with Benetoos — Where precision meets transparency.
🌐 Learn more at www.benetoos.com
📩 Contact us: benetoos.energy@gmail.com