Rashim Deepika
Artificial Intelligence (AI) is emerging as a game-changer across
multiple sectors, and its impact on the renewable energy domain is particularly transformative. The growing focus on clean energy, energy efficiency, and sustainable development calls for innovative solutions that can reduce costs, improve efficiency, and ensure timely project execution. AI has the potential to revolutionize the solar sector, hydro sector, solarization of government buildings, installation and commissioning of renewable energy plants, and even the monitoring and maintenance of infrastructure such as solar street lights. Its applications extend to decision-making, predictive analytics, and research evaluation, making it an important tool for Science and Technology Departments.
AI in Solar and Hydro Sector Purchases
Procurement of solar power plants, turbines, and associated equipment is often a time-consuming process involving multiple stakeholders. AI can streamline this process through AI-based tender analysis tools that evaluate bids based on cost, quality, delivery timeline, and vendor performance history. Predictive algorithms can also forecast future price trends of panels, inverters, and hydro turbines, helping decision-makers purchase equipment at the most economical time.
Additionally, AI can detect anomalies or unusually low bids that may indicate future quality or delivery issues, thereby reducing procurement risks.
Solarisation of Buildings and Plant Installation
AI plays a significant role in site selection and feasibility studies for rooftop solar projects and small hydro plants. By analyzing satellite imagery, weather patterns, shading data, and load requirements, AI recommends optimal capacity and panel placement for maximum generation. During installation, AI-driven project management tools allocate resources, schedule activities, and predict delays due to weather or logistical constraints.
Digital twin technology – a virtual simulation of the plant – allows engineers to visualize the installation process, detect design flaws, and ensure smooth execution before physical implementation. This results in faster, error-free commissioning and reduced project costs.
Commissioning and Performance Monitoring
AI-powered drones and computer vision systems can inspect solar plants for panel alignment, wiring quality, and microcracks during commissioning. Once operational, AI continuously monitors the output of each panel, turbine, or inverter. Any deviation from expected generation is immediately flagged, enabling timely intervention. Predictive maintenance models use real-time sensor data (temperature, vibration, voltage) to identify components at risk of failure, ensuring that repairs are carried out before a breakdown occurs. This minimizes downtime and improves the overall plant efficiency.
Role of AI in Solar Street Lights and Repair Monitoring
Solar street lights are critical for urban and rural infrastructure but often suffer from delayed repairs and maintenance. AI can automate real-time monitoring of street lights by using IoT-enabled sensors to report failures instantly. Predictive models can forecast battery failures or LED degradation, allowing pre-emptive replacement and reducing public inconvenience. AI-based dashboards can also monitor the performance of repair teams, track response times, and generate reports for administrative review. This ensures faster resolution and improved service delivery.
AI in Biomass Cookware and Energy Efficiency
AI can also contribute to the development and optimization of biomass cookware by analyzing combustion efficiency, fuel usage, and emission levels. Smart sensors connected to AI models can recommend optimal fuel loading patterns, thereby reducing smoke emissions and improving energy efficiency. This helps in promoting clean cooking solutions, especially in rural areas where biomass remains a major fuel source.
AI in Science and Technology Departments
Within Science and Technology Departments, AI can support evaluation of research papers and project proposals by screening for originality, relevance, and alignment with policy priorities. Natural Language Processing (NLP) tools can compare submissions against global databases to detect plagiarism, identify innovative keywords, and rank projects based on merit. AI can also help predict the potential impact of a project by analyzing historical data from similar research, thereby enabling evidence-based funding decisions.
Conclusion
Artificial Intelligence is no longer just a futuristic concept; it is an essential tool for improving efficiency, transparency, and sustainability in the renewable energy sector. From purchasing decisions to plant commissioning, from solarisation of buildings to monitoring solar street lights, AI ensures better resource utilization, reduced project delays, and improved service delivery.
(The author is presently posted as Financial Advisor, Science & Technology Department, UT of J&K)
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