Designing Bounded min-knapsack Bandits algorithm for Sustainable Demand Response (Papers Track) Spotlight

Akansha Singh (Indian Institute of Technology, Ropar); Meghana Reddy (Indian Institute of Technology, Ropar); Zoltan Nagy (University of Texas); Sujit P. Gujar (Machine Learning Laboratory, International Institute of Information Technology, Hyderabad); Shweta Jain (Indian Institute of Technology Ropar)

Paper PDF Slides PDF Recorded Talk Cite
Power & Energy Buildings Reinforcement Learning Uncertainty Quantification & Robustness


Around 40% of global energy produced is consumed by buildings. By using renewable energy resources we can alleviate the dependence on electrical grids. Recent trends focus on incentivizing consumers to reduce their demand consumption during peak hours for sustainable demand response. To minimize the loss, the distributor companies should target the right set of consumers and demand the right amount of electricity reductions. This paper proposes a novel bounded integer min-knapsack algorithm and shows that the algorithm, while allowing for multiple unit reduction, also optimizes the loss to the distributor company within a factor of two (multiplicative) and a problem-dependent additive constant. Existing CMAB algorithms fail to work in this setting due to non-monotonicity of reward function and time-varying optimal sets. We propose a novel algorithm Twin-MinKPDR-CB to learn these compliance probabilities efficiently. Twin-MinKPDR-CB works for non-monotone reward functions bounded min-knapsack constraints and time-varying optimal sets. We find that Twin-MinKPDR-CB achieves sub-linear regret of O(log T) with T being the number of rounds demand response is run.

Recorded Talk (direct link)