Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. In this paper, we demonstrate the application of an adaptive and explorative real-time optimization framework to an industrial refrigeration process, where we learn the process characteristics through changes in process control targets and through exploration to satisfy safety constraints. We quantify the uncertainty in unknown compressor characteristics of the refrigeration plant by using Gaussian processes and incorporate this uncertainty into the objective function of the real-time optimization problem as a weighted cost term. We adaptively control the weight of this term to drive exploration. The results of our simulation experiments indicate the proposed approach can help to increase the energy efficiency of the considered refrigeration process, closely approximating the performance of a solution that has complete information about the compressor performance characteristics.

Reinforcement learning is essentially an AI discipline in the sense that it tries to realize content that normally can only be realized by utilizing human …

Smart grid analytics are solutions utilized for analyzing a huge amount of data generated via smart grid systems. Smart grid analytics are employed for gaining an enhanced predictive evaluation of grid conditions and consumer behavior and hence help optimize the efficiency of grids. The prime factor stimulating the growth of the smart grid analytics market is the increasing investment in smart grid systems. Owing to the ever-increasing electricity demand, a number of utility providers are looking for reliable solutions for optimizing the efficiency of smart grids. This will augment the demand for smart grid systems in forthcoming years, thus fuelling the market for smart grid analytics.