Description:BACKGROUND OF THE INVENTION
Now a days there is increase in demand of energy which has led to the growing concerns towards the patterns of energy consumption and consumer-provider disputes, especially in the residential as well as the commercial sectors. Old traditional methods of the energy monitoring and billing often lack transparency, real-time accuracy and effective mechanisms of dispute resolution. Due to this inefficiency there are delayed detection of anomalies, consumer dissatisfaction and billing discrepancies. Hence there comes a need of an advanced and automated system (device) which can continuously monitors the energy usage which can provide real-time data to both providers and consumers and can identify any irregularities promptly. Integrating AI and IoT technologies in management of the energy to streamline this process offering a more efficient, transparent and responsive system. Our system aims at developing an AI-powered smart energy dispute resolution platform which is capable of managing and analyzing energy consumption in real-time, while minimising the human intervention in resolving the disputes regarding billing and other energy-related disputes.
The research area of dispute resolution and energy management has gained significant attention in today’s era because of rapid growth of energy consumption, coupled with increasing need of accurate billing, real-time monitoring and efficient dispute resolution mechanisms. While there are existing solutions such as traditional smart meters which provides some basic functionality like measuring consumption of energy and facilitating remote readings but fails in addressing more complex challenges such as energy management and dispute resolution. The primary gap is lack of integrated system which combines advanced analytics, real-time data collection and automated dispute resolution mechanism.
Absence of real-time, accurate data analytics for both energy providers and consumers is one of the significant gaps. Existing systems largely rely upon pre-set estimates and periodic readings, which can lead to inaccuracies in data of energy consumption. In today era consumer-feedback or manual inspection is required in case of billing disputes, which introduces delays and human errors in dispute resolution process. Although smart meters can provide real-time data and their infrastructure of communication (often based on simple data transmission) doesn’t allows for detailed and continuous analytics which makes it more difficult to detect the anomalies in energy usage at an early stage. Furthermore, there is a lack of intelligence in these systems to analyse discrepancies automatically and provide real-time insights in overconsumptions or billing disputes.
In addition, there is also a gap in addressing the complex energy usage patterns. Energy consumptions are highly variable and influenced by multiple factors which includes user behaviour, equipment malfunction and environmental conditions. These dynamic variables can’t be captured accurately by traditional systems. Even though machine learning and AI have been used in demand response and energy farecasting but there is limited research that integrates AI-driven anomaly detection for dispute identification or real-time billing adjustments. The lack of the prescriptive and predictive analytics in current systems is a critical research gap as patterns of energy consumption often follow unpredictable behaviors which makes it challenging for energy providers to offer dynamic pricing or prevent disputes before they arise.
Another critical gap is the lack of seamless integration between the different-different components of an energy monitoring system. Although, there exists technologies and devices (such as IoT sensors, smart meters and cloud platforms) which collects and monitors energy data, while there exists a gap in integration of AI algorithm and automated dispute resolution frameworks which is not widely explored. The research community lacks of the exploring how these diverse data sources can be combined in a cohesive system that supports not just real-time monitoring, but also provides a intelligent decision-making regarding dispute resolution and energy consumption.
Moreover, there remains a significant challenge of consumer engagement in energy dispute management. Traditional systems do not effectively empower consumers in tracking their own energy usage, analyse pattern or participate in processes of dispute resolution. There is still lack of research on user-friendly interfaces which allows consumers to interact with the system and receive the real-time notifications about discrepancies in their energy consumptions. Additionally, most of the traditional systems fails to provide transparency in showing how the billing calculations are made, which is most essential in for building trust between the consumers and the energy providers.
Lastly there exists research on the use of IoT-based smart meters for energy monitoring but there exists a lack of attentions towards development of the holistic systems that not only track consumption but also addresses the disputes proactively. Most of the existing research focuses upon energy demand forecasting or efficiency optimisation, with minimum attention paid to automatic error detection and resolution of disputes through the algorithms which are intelligent. Thus, there remains a significant gap in developing platforms which are AI-based which integrates real-time analytics, IoT sensors and consumer feedback to handle disputes in a transparent and autonomous manner.
In conclusion, there are substantial research gap in developing a comprehensive, AI-driven platform for real-time dispute resolution and energy monitoring. This platform would integrate advanced AI algorithms, data analytics and IoT sensors to provide both energy providers and consumers with transparent billing practices, automated dispute management and accurate insights. Addressing these gaps would not only improve accuracy and efficiency but also boosts the overall trust and transparency in energy consumption practices.
US11900493B2 Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media. One method includes: at a blockchain-based application, receiving a request for resolving a dispute between at least a first party and a second party. A time that the request is received on the blockchain is recorded. One or more potential dispute resolutions is received from one or more dispute resolution providers that are registered on the blockchain-based application. A first selection is received from the first party and a second selection from the second party. At least one of (i) at least one common potential dispute resolution between the first set of the one or more potential dispute resolutions and the second set of the one or more potential dispute resolutions, or (ii) that none of the potential dispute resolutions are acceptable to the first and second parties is determined.
RESEARCH GAP: Unlike the blockchain-based system focused on consensus-driven dispute resolution, our invention prevents disputes by using IoT sensors and AI to detect anomalies in real-time energy usage.
US20250166102A1 An AI-driven system for automated dispute resolution employs natural language processing to analyze claims and arguments, extracting semantic relationships to construct a structured data model. A reasoning module evaluates this model against a database of precedents and legal principles, generating decision scores for potential outcomes. The user interface presents visual representations of the analysis, allowing decision-makers to interactively explore and modify inputs. A decision recommendation module proposes resolutions based on criteria such as novelty, legal sufficiency, and compliance with jurisdictional laws.
RESEARCH GAP: Unlike AI systems based on semantic analysis of textual claims, our invention utilizes real-time sensor data and machine learning to detect and resolve physical energy consumption anomalies.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Artificial Intelligence Driven System for Energy Dispute Management
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The AI-powered Energy Dispute Management system (AI-EDMS) is designed such that it can address the growing concerns around bill discrepancies, energy consumption and disputes between energy providers and consumers. This system integrates Artificial Intelligence, Machine Learning, IoT sensors and smart metering infrastructure for providing accurate and real-time monitoring of energy usage, seamless dispute resolution and automated anomaly detection. The system is built to create an efficient, transparent and responsive energy consumption management platform which ensures both consumers and energy providers can interact effectively in case of discrepancies, ultimately leading to a reduction in energy waste, conflicts and increased satisfaction on both ends. This mechanism will address the key pain points of the current traditional energy management systems where there exist inaccurate readings, delays in data reporting and lack of proactive dispute management which often causes frustration for both providers and consumers.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: OVERALL ARCHITECTURE
FIGURE 2: SMART NUMERIC SENSORY DATA COLLECTOR The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The AI-powered Energy Dispute Management system (AI-EDMS) is designed such that it can address the growing concerns around bill discrepancies, energy consumption and disputes between energy providers and consumers. This system integrates Artificial Intelligence, Machine Learning, IoT sensors and smart metering infrastructure for providing accurate and real-time monitoring of energy usage, seamless dispute resolution and automated anomaly detection. The system is built to create an efficient, transparent and responsive energy consumption management platform which ensures both consumers and energy providers can interact effectively in case of discrepancies, ultimately leading to a reduction in energy waste, conflicts and increased satisfaction on both ends. This mechanism will address the key pain points of the current traditional energy management systems where there exist inaccurate readings, delays in data reporting and lack of proactive dispute management which often causes frustration for both providers and consumers.
This AI- powered energy dispute management system starts with the smart meter installation at consumers’ place. These meters are equipped with a range of IoT sensors which continuously monitors critical parameters such as current, power consumption, voltage temperature and even also monitors environmental factors such as air quality or humidity that might indirectly affect the usage of energy. The system begins its work by continuously and constantly collecting the data from the deployed sensors. The sensors include current transformers for measurement of the current while voltage sensors are used to track the voltage supplied to the consumer, Hall Effect Sensor is used to detect the magnetic field produced by the current in the wire for measuring current without direct contact, Shunt sensor is used for measuring the current by detecting voltage drops across the resistor in a circuit, Power calculation IC is used to directly measure active, reactive, and apparent power, Energy metering IC is used to ICs integrate both current and voltage measurements and calculate power consumption, Temperature and Humidity sensors are used to collect environmental factors. These sensors feed the data collected to the ESP32 for processing the incoming data and then transmits the data to the cloud server for further processing
One of the key components of the system is the AI algorithm which is embedded with the cloud server. The collected data such as power surges, current spikes, voltage fluctuations and sudden dips are all analysed using ML models which can detect anomalies in real-time. These anomalies could be caused by various factors such as equipment malfunction, human error, electrical faults and fraudulent activities. For example, if there is sudden higher consumption than the usual without any apparent reason, the AI system can flag this as anomaly. This can happen automatically without any intervention of the human being, ensuring that any discrepancies in the energy consumption are flagged as soon as they take place hence minimizes the chances of issues escalating into disputes.
The real-time monitoring of the system which are empowered by the continuous data stream which allows immediate detection of unusual behaviour such as billing anomalies and overuse of energy. In cases where there is a discrepancy detected between the expected usage (based on consumer habits, historical data and environmental conditions) and the actual consumption recorded, the system is made such it will immediately alerts the both energy provider as well as the consumer of the energy. For example, if there is sudden surge in consumption of energy detected by the system at a particular time when the consumer is supposed to be using less energy, the system will generate an alert and sends a notification to the user via mobile app and a notification is also sent to the energy provider on web dashboard and the issue can be investigated further.
The communication between the microcontroller and the cloud infrastructure is facilitated by using Wi-Fi. This ensures that the system can work in different environments efficiently. The data collected or generated by the sensors is continuously transferred to cloud server where it is processed further and stored for future use. The cloud platform also works as a hub for data which is energy-related by enabling both real-time monitoring and historical trend analysis. This central database can store detailed logs of fault occurrence, energy consumption, billing discrepancies and dispute resolutions, ensuring that both the provider and the customer have access to a tamper-proof and transparent history of transactions.
For improving the accuracy of billing and reducing errors, the system integrates predictive analytics into its functionality. The AI-based platform utilizes accumulated data for generating predictive models which will forecast the patterns of energy consumption of the consumer. These models take in account factors such as past consumption patterns, seasonal usage variations and environmental data such as time of day, temperature fluctuations etc. By these data the system is able to predict when the energy consumption will be higher than usual and will adjust the billing rates accordingly. Also, the while there is peak demand of energy such as at the period when there are severe summers or cold when there are requirements of air-conditioning and heaters is more, the system can predict the increased consumption and also can adjust the billing ensuring that the consumer is being charged only for actual usage and hence prevents overcharges. This ensures accurate billing, as the system removes discrepancies before they reach the final billing cycle.
If any dispute arises regarding to power consumption the AI-platform can trigger a dispute resolution protocol which allows both provider and consumer to investigate the issue further. The system can provide provider and consumer with an audit trail of energy consumption, providing clarity and transparency. This audit trial is stored in the cloud server which provides a time-stamped record of all measurements, alerts, anomalies and any adjustments made to the billing. This information serves as a reference point for resolving the disputes by reducing the human intervention in resolving process. If the dispute is not resolved automatically then the system provides an easy interface for both the parties to engage in dispute resolution process. Consumer can review their energy usage through the system’s user interface, while the energy provider can make adjustments if necessary. In some cases, simple disputes can be handled by automated algorithms, such as when consumption is slightly higher than expected due to environmental factors which can provide the consumers a bill which is adjusted instantly.
In the case of major inconsistencies, the system allows for the intervention of humans by providing a clear, data-backed case for the customer support team to resolve the issue. This transparency results in boosting the dispute resolution process by enabling both parties to already have the complete records of the things happening which are leading to the disagreement. The involvement of AI while streamlining the process also eliminates the human error which is common in traditional dispute handling process.
Blockchain technology is being used which guarantees the allows the data recorded from the smart meter be stored in an immutable and decentralised ledger which guarantees that no data can be altered or falsified after it is recorded once. The system’s ability to integrate with the existing energy meters in commercial and residential, retrofitting them with communication modules and IoT sensors, ensuring minimal disruption during installation. This compatibility with both existing and new energy infrastructure makes the solution highly applicable and scalable to a wide range of energy consumers, from individual households to large industrial units.
The consumer engagement aspect of the system is also a critical component of the working mechanism. Web interfaces or mobile apps provides consumers with real-time feedback on their energy consumption which allows them to check and track their usage patterns and compare them with predicted values. These interfaces are helpful to the users in the way to track the usage pattern, which potentially leads to energy conservation and reduced costs. Consumers can set the energy usage goals and also receives the alerts about overuse and also can get advice on how to cut down their energy consumption during peak periods. Also, personalised reports on energy consumption can help the consumers in making more informed decisions about their energy usage which encourages them to adopt more sustainable practices.
As a result of this integrated mechanism, the AI-powered energy dispute management system provides a comprehensive solution to modern energy management challenges. It leverages predictive analytics, automated anomaly detection, real-time data and AI-powered dispute resolution which ensures that disputes are minimized, energy consumption is accurately monitored and both providers and consumers benefit from a efficient and transparent system. This innovative approach not only resolves existing challenges in energy management but also sets the foundation for a more consumer-centric and sustainable future.
The algorithm begins by collecting real-time energy data from IoT-enabled smart meters, measuring parameters like voltage, current, and power usage. This data is preprocessed through normalization, feature extraction, and time-series analysis. A machine learning model, such as Random Forest, is trained on historical consumption data to detect anomalies and predict future usage. When abnormal patterns are detected, the system triggers alerts and suggests billing adjustments. Disputes are resolved either automatically using AI or escalated with full data transparency. The model continuously learns from new data through periodic retraining for improved accuracy.
Pseudo Code
# Step 1: Data Collection from IoT Sensors and Smart Meters
Function collect_data():
Initialize smart_meters with sensors (Current, Voltage, Power, Temperature, Humidity)
Initialize communication_protocol (WiFi)
while True:
Read data from sensors (Current, Voltage, Power, Temperature, Humidity)
Timestamp the data
Send data to cloud server for processing
Sleep for specified time interval (e.g., 1 minute)
# Step 2: Data Preprocessing
Function preprocess_data(data):
# Handling Missing Data
If data has missing values:
Fill missing data using imputation (mean/median/last valid value) or remove rows
# Normalize data
Normalize data to scale it between 0 and 1
# Feature Engineering (e.g., rolling averages, time-based features)
Add rolling averages of power consumption
Add time-of-day, hour-of-day, and daily usage patterns

# Time-Series Decomposition (if applicable)
Decompose time-series data into trend, seasonal, and residual components
Return processed data
# Step 3: Train Machine Learning Model
Function train_model(training_data):
# Select the algorithm for anomaly detection and prediction (e.g., Random Forest)
Initialize ML_algorithm (Random Forest)
# Split data into training and test datasets
Split data into training set and test set (80% train, 20% test)
# Train the model using the training dataset
Train ML_algorithm with training_data
# Evaluate the model performance on the test set
Evaluate the model on test_data (Accuracy, Precision, Recall, F1-Score)
Return trained_model
# Step 4: Anomaly Detection and Prediction
Function detect_anomalies_and_predict(model, incoming_data):
# Preprocess incoming data
processed_data = preprocess_data(incoming_data)
# Predict if incoming data is normal or anomalous (using trained ML model)
predictions = model.predict(processed_data)
If predictions indicate anomaly:
# Alert consumer and provider
Send anomaly alert to consumer and provider
Log anomaly details (timestamp, predicted anomaly type, data)
Return predictions
# Step 5: Automated Billing Adjustments Based on Predicted Consumption
Function adjust_billing(predicted_data, actual_data):
# Compare predicted energy usage with actual consumption
If predicted usage > actual usage:
# Apply discount or adjust bill
adjusted_bill = actual_data.billing - calculate_discount(actual_data)
Else if predicted usage < actual usage:
# Apply surcharge or adjust bill
adjusted_bill = actual_data.billing + calculate_surcharge(actual_data)
Else:
adjusted_bill = actual_data.billing
# Update billing system with adjusted bill
Update the consumer's bill with adjusted_bill
Return adjusted_bill
# Step 6: Dispute Resolution
Function handle_dispute(predicted_data, actual_data, consumer_data):
# If consumer raises a dispute, check anomaly
If consumer disputes bill:
# Log dispute
Log dispute details with time, cause, and resolution steps
# Check for anomaly
anomaly = detect_anomalies_and_predict(trained_model, actual_data)
If anomaly exists:
# Generate automated resolution suggestion
suggested_resolution = resolve_dispute_based_on_anomaly(anomaly)
# Send suggestion to both consumer and provider
Send dispute resolution suggestion to consumer and provider
Else:
# If no anomaly detected, escalate to human support
Escalate dispute to customer support team with data logs
Return "Dispute handled successfully"
# Step 7: Continuous Improvement (Retraining Model with New Data)
Function retrain_model():
# Collect new data from sensors
new_data = collect_new_data()
# Preprocess the new data
processed_new_data = preprocess_data(new_data)
# Retrain the model with updated data
trained_model = train_model(processed_new_data)
Return trained_model
# Main Workflow
Function main():
# Step 1: Start collecting data from IoT sensors and smart meters
Start collect_data()
# Step 2: Initialize and train the model
training_data = collect_initial_data()
trained_model = train_model(training_data)
# Step 3: Start real-time monitoring and anomaly detection
while True:
incoming_data = collect_data()
predictions = detect_anomalies_and_predict(trained_model, incoming_data)
If predictions indicate anomaly:
# Step 4: Adjust billing and notify consumer and provider
predicted_data = generate_predicted_data(incoming_data)
adjusted_bill = adjust_billing(predicted_data, incoming_data)
# Step 5: Handle dispute if any arises
If consumer_disputes_bill:
handle_dispute(predicted_data, incoming_data, consumer_data)
# Step 6: Periodic retraining for continuous improvement
If time_to_retrain:
trained_model = retrain_model()
Sleep for specified interval (e.g., 10 minutes)
ADVANTAGES OF THE INVENTION
• The system monitors energy usage continuously through IoT-enabled smart meters and immediately detects unusual consumption, energy theft and prevents billing errors.
• Due to integration of AI algorithms, disputes related to billing are resolved automatically using data-driven insights, significantly reducing response time, consumer-provider conflicts and human intervention.
• Machine learning models predicts and validate energy usage by ensuring the bills to be adjusted dynamically based on real consumption patterns, enhancing trust and transparency.
• Consumers are able to receive real-time alerts via web interfaces or mobile by enabling proactive monitoring of their usage, better energy management and early detection of issues.
• The system uses scalable architecture, secure cloud storage and blockchain logging to manage large datasets by ensuring data integrity and supports integration across varied consumer segments.

, Claims:1. A smart energy metering system comprising a multi-sensor array configured to measure real-time electrical parameters including voltage, current, and power, along with environmental parameters such as humidity and temperature, wherein the device integrates an ESP32 microcontroller for signal processing and edge-level anomaly detection.
2. The system as claimed in claim 1, wherein the data collected from the multi-sensor array is transmitted securely via a wireless module including Wi-Fi to a cloud system for enabling remote monitoring and storage.
3. The system as claimed in claim 1, wherein the system provides tamper-resistant logging of data and supports edge computation for enhancing accuracy in energy management.
4. The system as claimed in claim 1, wherein an automated dispute management system is integrated, the system comprising an artificial intelligence engine linked with a billing module, wherein the engine detects anomalies in energy usage and triggers corrective actions including billing adjustments or credit allocation based on inferred causes.
5. The system as claimed in claim 1, wherein the automated dispute management system generates alerts for both the energy provider and the consumer, and maintains a digital audit trail for transparent resolution of disputes.
6. The system as claimed in claim 1, wherein a self-learning energy management system is configured with machine learning models retrained periodically using newly acquired data based on time or performance triggers, wherein validated updates are deployed automatically to adapt to changing usage patterns.
7. The system as claimed in claim 1, wherein the self-learning models improve decision accuracy in billing, anomaly detection, and dispute resolution.
8.The system as claimed in claim 1, wherein an artificial intelligence algorithm is configured to preprocess multi-sensor energy data and apply supervised and unsupervised models including Isolation Forest for anomaly detection and Random Forest for predictive billing.
9. The system as claimed in claim 1, wherein the algorithm flags conditions including overuse, tampering, faults, billing discrepancies, and initiates corresponding alerts.
10. The system as claimed in claim 1, wherein the system integrates IoT-enabled smart meters, cloud-based analytics, predictive modeling, and blockchain logging to ensure transparent billing, real-time monitoring, automated anomaly detection, and low-intervention dispute resolution.