Our research group is proud to contribute to advancing AI-driven smart control and automation through impactful research published in leading journals. Two recent papers highlight our work in leveraging artificial intelligence for energy efficiency and smart irrigation, attracting significant media attention.
AI for Energy Efficiency
The paper titled “A Data-Driven Robust Optimization Approach to Scenario-Based Stochastic Model Predictive Control” published in Journal of Process Control has drawn some media attentions. These include, but are certainly not limited to, the following ones:
- Cornell Chronicle: To conserve energy, AI clears up cloudy forecasts
- World Economic Forum: New machine learning methods can slash energy bills in old homes
- Energy Manager Today: Cornell Using Machine Learning to Aid in Energy Savings
- Engerati: Harnessing weather forecasting for building energy efficiency
- Market Business News: AI conserves energy by making better use of weather forecasts
- Engineering: Hot New Research Uses Machine Learning to Help Buildings Save Energy
- Technology Networks: Data-driven Approach Saves Energy Whatever the Weather
- Tech2: Machine learning allows the old building to use less heat
- TechZiffy: Energy Conservation Made Easy Through Machine Learning
- Asharq Al-awsat: Scientists Develop New AI System to Predict Weather Changes
- Tencent News
AI for Smart Irrigation Systems
Our paper “A Data-Driven Robust Optimization Approach to Scenario-Based Stochastic Model Predictive Control” published in IEEE Transactions on Control Systems Technology, demonstrates how AI can optimize water use in agriculture. This work has been featured in:
AI for Greenhouse Energy Efficiency
Our research group has contributed to advancing sustainability in agriculture through the development of an AI-based controller that reduces greenhouse energy consumption by 30%. In a recent Applied Energy paper by Akshay Ajagekar (pictured below), this innovation was tested at Guterman Lab in Ithaca and a five-greenhouse system in New York City, optimizing variables such as temperature, humidity, and lighting to balance energy savings and productivity.
This work highlights the potential for scalable, cost-effective solutions in controlled environment agriculture and has been featured in the Cornell Chronicle for its implications in energy efficiency and sustainable food production.
This research complements our 2024 Nature Food paper, led by Dr. Benjamin Decardi-Nelson, which demonstrated how AI can regulate light and climate systems in plant factories to reduce energy use while ensuring high crop yields. Together, these studies showcase the transformative potential of AI in advancing sustainable agriculture across diverse indoor farming systems.
