AI-Powered Smart Panchayats Empowering Villages Through Rural Energy & Healthcare

AI-Powered Smart Panchayats Empowering Villages Through Rural Energy & Healthcare

Mr. Chethan R

Assistant Professor, dept. of EEE, G. Madegowda Institute of Technology. Bharathinagara, Mandya, India.

Mr. Ravi Kumar K N

Assistant Professor, dept. of EEE, G. Madegowda Institute of Technology. Bharathinagara, Mandya, India.

Akshay H S

Student, dept. of EEE, G. Madegowda Institute of Technology. Bharathinagara, Mandya, India.

Jahnavi K S

Student, dept. of EEE, G. Madegowda Institute of Technology. Bharathinagara, Mandya, India.

Manjunath G Y

Student, dept. of EEE, G. Madegowda Institute of Technology. Bharathinagara, Mandya, India.

Abstract – Rural governance systems face persistent challenges due to manual infrastructure monitoring, limited healthcare awareness, and inefficient grievance redressal mechanisms. These issues result in delayed responses, lack of transparency, and reduced service quality at the Panchayat level. This work presents an AI-powered Smart Panchayat system that integrates rural energy monitoring, healthcare awareness, and digital grievance redressal into a unified web-based platform. The proposed system enables real-time visualization of feeder performance, structured complaint tracking, and AI-assisted non-diagnostic health advisory. Ethical and explainable AI techniques are employed to support decision-making without replacing human authority. Experimental results using representative rural sub-station data demonstrate improved monitoring efficiency, transparency, and citizen participation. The Smart Panchayat platform provides a scalable and cost- effective solution for technology-enabled rural governance.

Keywords – Smart Panchayat, Rural Governance, Energy Monitoring, Healthcare Awareness, Grievance Redressal, Artificial Intelligence

INTRODUCTION

Rural Panchayats serve as the backbone of grassroots governance, managing essential services such as electricity distribution, public grievance handling, and basic healthcare awareness. However, most Panchayat-level systems continue to rely on manual record-keeping, delayed reporting, and fragmented service platforms. In rural electricity networks, the absence of real-time feeder monitoring leads to prolonged outages and inefficient maintenance. Similarly, lack of

preventive healthcare awareness and informal grievance mechanisms reduce service effectiveness and public trust.

Recent advancements in web technologies, data visualization, and artificial intelligence offer new opportunities to modernize rural governance. Smart governance platforms can enable transparency, real-time monitoring, and citizen participation. This work proposes a Smart Panchayat system that integrates energy monitoring, healthcare advisory, and grievance management into a single digital framework suitable for rural deployment.

BACKGROUND AND RELATED WORK

1. Literature Survey

   Recent studies on digital governance highlight the importance of web-based platforms in improving transparency and accountability in rural administration. Research on smart energy monitoring systems demonstrates that feeder-level visualization and fault analysis significantly improve maintenance efficiency in rural power networks. Similarly, studies on AI-based healthcare awareness tools indicate that non-diagnostic advisory systems can effectively promote preventive healthcare in resource-constrained rural regions.

   Existing grievance redressal systems in rural governance often suffer from lack of transparency and delayed response due to manual processing. Literature emphasizes the need for centralized digital dashboards and structured workflows to

   improve grievance handling efficiency and citizen trust. These findings collectively motivate the development of an integrated Smart Panchayat system.

2. Existing Governance Approaches

   Traditional rural governance approaches rely heavily on manual data collection, periodic inspections, and informal communication channels. While some digital solutions exist, they are often limited to single services and lack integration. The absence of a unified platform combining energy monitoring, healthcare awareness, and grievance management highlights the need for a comprehensive Smart Panchayat framework.

   1. Step 5: Performance Evaluation

      The system is evaluated based on functional accuracy, responsiveness, and usability. Real-time updates, correctness of feeder status, grievance tracking accuracy, and clarity of healthcare advisory outputs are analyzed to assess overall system performance and practical usability.

      1. PROPOSED WORK

         The proposed work focuses on designing an intelligent, integrated, and user-friendly Smart Panchayat platform that supports transparent rural governance and informed decision- making.

METHODOLOGY

The methodology of the proposed AI-powered Smart Panchayat system integrates structured data handling, preprocessing, rule-based analysis, AI-assisted logic, and visualization to ensure transparent and efficient rural governance. The system is designed to support real-time rural energy monitoring, healthcare awareness, and digital grievance redressal at the Panchayat level.

Step 1: Data related to rural electricity feeders, citizen grievances, and healthcare inputs are collected through web- based interfaces and administrative sources. The collected data includes feeder load values, interruption records, grievance descriptions, and basic health parameters. Incomplete, duplicate, or inconsistent records are filtered to improve data reliability. The cleaned dataset is organized into structured formats for processing and visualization.

Step 2: Data Preprocessing

To ensure consistency and accuracy, preprocessing is applied to all collected data. Electrical parameters are normalized and time-stamped, grievance records are categorized based on issue type, and health inputs are validated against predefined ranges. Noise and redundant entries are removed to improve system reliability. This preprocessing stage ensures uniform and clean input for subsequent analysis modules.

Step 3: Feature Extraction and Analysis

Relevant features such as feeder current levels, fault categories, grievance status, and health risk indicators are extracted from the preprocessed data. Rule-based and threshold-based logic is applied to identify abnormal feeder conditions, frequently occurring grievances, and potential health risk patterns. This approach enables effective analysis while maintaining explainability and ethical use of Artificial Intelligence.

Step 4: Decision Support and Visualization

The analyzed features are mapped to decision-support outputs. Electrical data is visualized using feeder matrices and load charts, grievance data is displayed through status dashboards, and healthcare awareness outputs are generated as advisory messages. These visualizations enable Panchayat officials and citizens to easily interpret system outputs.

1. Data Acquisition

   Data is obtained from rural sub-stations, Panchayat grievance submissions, and user-provided health inputs. The dataset includes feeder current values, interruption logs, complaint descriptions, and basic health parameters. The collected data represents real-world rural operational conditions.

2. Data Enhancement and Preprocessing

   Preprocessing ensures uniformity across all system modules. Electrical data is normalized, grievance records are structured, and health inputs are validated. Enhancement techniques improve data clarity and consistency, enabling accurate analysis and visualization.

3. AI-Assisted Analysis

   The enhanced data is processed using rule-based and threshold-based AI logic. The energy monitoring module detects abnormal load conditions and fault trends, the grievance module identifies unresolved issues, and the healthcare module provides non-diagnostic advisory outputs. The AI logic is designed to support administrators rather than replace human judgment.

4. Decision Output and User Interaction

   Based on analyzed data, the system generates clear outputs for users and administrators. Feeder conditions, grievance status, and health advisories are displayed with simple labels and indicators. The interface is designed to minimize user effort and improve accessibility for rural users.

5. System Architecture Description

The system architecture consists of a public interface module, data preprocessing module, AI-assisted analysis module, centralized database, and visualization dashboard. Smooth communication between modules ensures real-time execution and minimal delay.

1. 1. 1. RESULTS AND PERFORMANCE ANALYSIS

         The Smart Panchayat system is evaluated based on its functional performance and ability to support rural governance activities. Testing confirms that the system successfully integrates energy monitoring, grievance handling, and healthcare awareness into a unified platform.

         1. Energy Monitoring and Visualization Results

            The system accurately displays real-time feeder load values and operational status through dashboards and feeder matrices. Load variations and abnormal conditions are clearly visualized, enabling proactive maintenance and fault identification.

            supported with preventive guidance, encouraging early medical consultation without diagnostic claims.

            Fig. 1. Real-time feeder monitoring dashboard showing load status and operational condition

         2. Digital Grievance Redressal Results

            The grievance module successfully records citizen complaints and tracks their resolution status. Complaints are categorized and displayed as pending or resolved, improving transparency and accountability in Panchayat administration.

            Fig. 3. AI-assisted healthcare advisory and risk awareness output

            D. Performance Discussion

            The results demonstrate that integrating structured data preprocessing, AI-assisted logic, and visualization enables efficient and transparent rural governance. The system effectively supports decision-making while maintaining simplicity and explainability. With further data expansion and module enhancement, system performance can be improved further.

            Fig. 2. Digital grievance submission and status tracking interface

         3. Healthcare Awareness and Risk Advisory Results

         The healthcare module provides advisory information based on user inputs. Risk estimation outputs are clearly labeled and

      2. CONCLUSION

The increasing complexity of rural governance demands digital solutions that enhance transparency, efficiency, and citizen participation. This paper presented an AI-powered Smart Panchayat system that integrates rural energy monitoring, healthcare awareness, and grievance redressal into a unified web-based platform. Experimental evaluation shows that the system effectively supports real-time monitoring, transparent complaint handling, and preventive healthcare

awareness. By acting as a decision-support tool rather than an autonomous system, the proposed platform ensures ethical AI usage and practical deployment. Overall, the Smart Panchayat system demonstrates strong potential to improve grassroots governance and contribute to sustainable rural development.

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