How AI-powered BMS and predictive maintenance drive net zero in rail and metro systems

Climate change is an undeniable threat, demanding immediate action, temperatures in nearly half of the country are soaring 3-10 degrees celsius above normal. Despite the optimism surrounding the Paris Agreement, the harsh reality is that we are on a trajectory to breach the 1.5 degrees celsius limit for global temperature. The transportation sector is a significant contributor to greenhouse gas emissions with road transport presently accounting for 12% of India’s energy-related CO2 emissions. On the other hand, while the Indian Railways stand as the crown jewel of the nation's transportation sector, boasting the fourth largest railway network in the world with a daily passenger count of 24 million passengers, it too carries a notable environmental footprint. The rail and metro systems offer a clear advantage over private vehicles by reducing reliance on personal cars, achieving net zero emissions within their infrastructure remains a challenge.

Recognizing this, the Central Government has set an ambitious goal to decarbonize the Indian Railways by 2030. A major development to note is the Mumbai-Ahmedabad high-speed rail project, sanctioned in 2023, which is India's first bullet train project and will be entirely electric. Decarbonizing the Indian Railways is not just an environmental imperative, but also an economic one. Reduced reliance on fossil fuels can lead to significant cost savings, while also enhancing India's energy security. This ambitious plan involves a multi-pronged approach, including electrification of the vast network, adoption of renewable energy sources, and energy efficiency measures.

The gap between theoretical energy efficiency potential and real-world operational performance in rail and metro facilities presents a significant hurdle. Rail and metro systems are complex networks with numerous stations, depots, and maintenance facilities. Optimizing energy consumption across this vast infrastructure can be a daunting task. Here's where Artificial Intelligence (AI) steps in, offering a powerful solution to bridge this efficiency gap and propel the industry towards net zero goals.

BMS and the Power of AI

AI and Building Management Systems (BMS) in Rail and Metro Building Management Systems are the backbone of rail and metro facilities, overseeing functions like ventilation, lighting and heating. By harnessing real-time data and machine learning, AI-powered BMS can significantly enhance energy efficiency and sustainability.

However, implementing AI in this sector faces challenges. Integrating AI across extensive networks requires substantial investment and expertise. Additionally, gathering, standardizing, and utilizing accurate data is crucial but demanding. A shortage of skilled AI professionals further complicates the process.

To unlock AI's full potential, collaboration between technology providers, rail operators, and policymakers is essential. Robust data infrastructure and a skilled workforce are equally important. With these elements in place, AI can revolutionize rail operations, optimizing train schedules and reducing energy consumption.

Demand-based ventilation and cooling

Traditionally, during peak hours of a metro station, the HVAC system might operate at full capacity, needlessly consuming energy. Ensuring passenger comfort while optimizing energy consumption is a delicate balance. Overly aggressive energy-saving measures might lead to discomfort, while prioritizing comfort could negate energy savings. An AI-powered BMS changes this by analyzing real-time data on factors like passenger occupancy, weather conditions, and time of day. This allows the system to intelligently adjust ventilation and cooling intensity based on actual needs. Sensors continuously monitor temperature and CO2 levels, ensuring a healthy and comfortable environment even with reduced energy consumption. This demand-based approach ensures optimal energy use without sacrificing passenger comfort.

Predictive maintenance for extended lifespan

AI-powered BMS also excels in predictive maintenance by analyzing sensor data from HVAC equipment. By identifying potential issues like failing compressors or clogged filters before they escalate into major breakdowns, AI enables timely interventions. This proactive approach prevents disruptions and the associated energy waste caused by inefficient operation. Additionally, predictive maintenance extends equipment lifespan, reducing long-term costs for metro operators.

Sensors installed on trains and railway infrastructures can help monitor various parameters such as vibration, wear, and temperature. Further, the AI algorithms can analyze this data in real-time and enable timely identification of potential maintenance issues, in turn helping reduce breakdowns and improve overall reliability of the system.

For instance, the Dedicated Freight Corridors (DFCs) project is a cornerstone in India's railway modernization efforts. By segregating freight traffic from passenger trains, it not only enhances efficiency but also provides a fertile ground for advanced technologies like predictive maintenance.

The net zero pathway: quantifiable benefits and real-world success

The potential impact of AI-powered BMS and predictive maintenance on energy savings is significant. A 2024 research emphasizes on the fact that intelligent systems in the transportation sector have been shown to cut carbon dioxide emissions by approximately 60%, offering a significant leap toward cleaner urban environments.

These projections are finding support in real-world implementations. For instance, the Bengaluru Metro Rail Corporation (BMRCL), a pioneer in adopting sustainable practices in India, reportedly piloted an AI-powered energy management system in 2023. Initial results suggest promising reductions in energy consumption at select stations. The benefits extend beyond just energy savings. Reduced operational costs for rail and metro operators arise from optimized energy use and proactive maintenance practices. Additionally, improved infrastructure reliability due to predictive maintenance translates to fewer disruptions and delays, enhancing the passenger experience.

Collaboration and the road ahead

Unlocking the full potential of AI for achieving net zero in rail and metro systems necessitates a collaborative effort. Technology providers must continuously develop innovative AI-powered BMS and predictive maintenance solutions tailored to the industry's specific needs. Rail and metro operators play a vital role in implementing these technologies and integrating them effectively into existing infrastructure. Policymakers can significantly influence progress by establishing clear sustainability goals, providing financial incentives, and creating regulatory frameworks that promote the adoption of AI solutions. This three-pronged approach, driven by technological advancements, industry buy-in, and supportive policies, will pave the way for a future where AI empowers rail and metro systems to achieve net zero emissions and contribute to a more sustainable transportation landscape.

Conclusion

The concept of achieving net zero requires addressing several challenges. However, AI has the potential to be a powerful tool in the fight against climate change. Through its application, AI can enable sustainable development, enhance climate change mitigation and adaptation efforts, and contribute to the realization of a net-zero emissions future. AI-powered BMS and predictive maintenance offer a powerful solution to bridge the efficiency gap and achieve net zero goals for rail and metro systems. These tech-driven solutions have the potential to create a more sustainable and efficient future for urban transportation. By harnessing the power of AI, we can create cleaner, more reliable rail and metro networks, ensuring a greener future for generations to come.

Shankar Iyer
Director of Transportation, Smart Cities, and Sustainable Infrastructure
Johnson Controls, India