Cognitive IoT for Sustainability - Projects

  • Photonic Harvesting

Core team : Sukanya Randhawa, Amar Prakash Azad, Kalyan Dasgupta, and Manikandan Padmanaban

Collaborators: Pankaj Dayama, Nitin Singh

Solar energy is getting very high attention in many countries including India at an unprecedented growth rate with the green energy drive. Presently, the commodity solar PV system transforms only about 15% of the actual solar power into electrical power. At IRL, we came out with a disruptive innovation, namely “Photonic Harvesting”, where we push the solar power transformation up to 100% to the over legacy PV technology by utilizing simple but powerful tricks of optical cut and paste. We developed a cognitive IoT driven cloud solution aiming to increase the commodity PV efficiency up to 33%, almost equaling the efficiency factors of energy sources such as coal, petroleum and nuclear. Our composite solution optimizes the efficiency of PV power production taking into account various farm level engineering aspects such as ground coverage (GCR) tradeoff, geographical considerations such as solar azimuth, latitude, etc,. Our modelling also includes various other factors such shadowing, soiling using advance image analytics and machine learning techniques. Our solution is aiming to reduce the LCOE significantly and has got high traction by various solar giants, such as Adani, Softbank, etc.


  • Precision Agriculture

Core team: Ranjini Guruprasad, Mohit Jain, Sambaran Bandyopadhyay, Aanchal Aggarwal, Kamal C Das

Precision Agriculture (PA) is evolving much faster than expectations across the globe! One of the key objectives of PA is to increase the farm productivity by increasing the visibility of agronomic states (such as soil moisture, crop health, weather, etc.) of the farms leveraging digitization, mobile, IoT and cognitive technologies. Typical farming practices could not take preventive measures in time due to lack of visibility into the dynamic agronomic state. Manual tracking of such dynamic state is not only tedious but also hard to scale. Fortunately, many of these dynamic agronomic factors can be monitored through remote or hyper-local sensing and the new generation of sensors available today. Towards this goal, we are working on developing a suite of solutions (pest risk prediction, weeds detection, yield prediction, precision irrigation advisory services, etc) leveraging the power of IBM Research’s big data platform called PAIRS. PAIRS platform is used to ingest, curate and combine multiple global satellite based information sources to compute agronomic insights at a fine-grained sub-acre level. Furthermore, we are leveraging the power of mobile smartphone technology to capture field images and applying deep learning and advance image analytics to bring in actionable insights on time. These precision insights are then being delivered to the farmers/agronomists on the fields using smartphones to manage scarce groundwater-based irrigation, optimize the timing/amount of fertilizers & pesticides for the right part of their farm, etc. To validate some of these technologies, team is currently engaged with TATA Rallies –a subsidiary of TATA chemicals. Many more commercial engagements are in the pipeline including M&M, Sinarmas forestry, NSTADA, etc.

Agri Demo

Agri Camera



  • OPTi - Optimization of District Heating & Cooling Systems

Core team: Ramachandra Kota, Kumar Saurav, Sambaran Bandyopadhyay

Collaborators: Anamitra R Choudhury

The OPTi project aspires to create a long-lasting impact by rethinking the way DHC systems are architected and controlled. The overarching goal is to create business benefit for the industry as well as to ensure optimal end-consumer satisfaction. OPTi seeks to deliver methodologies and tools that will enable accurate modelling, analysis and control of current and envisioned DHC systems. OPTi treats the DHC system as a system subject to dynamic control, and will treat thermal energy as a resource to be controlled for DHC systems towards saving energy and reducing peak loads, overall providing a socio-economically sustainable environment.
       IBM Research, India is coordinating this project with seven EU partners and driving the overall scientific innovation of the project. In this project, IBM has developed a thermal load forecasting system (the 'black box' model) for DHC networks, a physics based thermal model of buildings (the 'grey box' model), and contributed to an automated demand response system for peak load management. Additionally, a simulator called OPTi-Sim is being developed by all the partners as part of the project which includes detailed models of the water network, associated control devices (valves, pumps, etc), building models, and so on. Physical trials are being conducted on the DHC networks of the utility partners - Lulea Energy in Sweden and Sampol in Spain.