Colonial Pipeline has partnered with Accenture to optimize utility rates using artificial intelligence (AI). Accenture is using a proprietary database powered by AI to help Colonial Pipeline, the largest refined products pipeline in the United States, reduce regulated and deregulated electric utility rates for its interstate pipeline system. The energy-management project leverages Accenture's Utility Tracking System (UTS), a proprietary database of approximately 30 million anonymized utility bills that the company has been aggregating for more than 20 years, according to a July 14 statement. Built to identify power tariff options around the world, UTS uses AI-powered insights and automation as part of Accenture's SynOps platform to continuously improve the efficiency and reliability of electricity rate-savings recommendations. Accenture is using insights generated by UTS to evaluate power bills for operations at approximately 80 Colonial Pipeline pump stations along its 5,500-mile pipeline system, which delivers approximately 100 million gallons of refined petroleum products daily to markets in the Southern and Eastern United States.

Resource allocation in computing clusters is traditionally centralized, which limits the cluster scale. Effective resource allocation in a network of computing clusters may enable building larger computing infrastructures. We consider this problem as a novel application for multiagent learning (MAL). We propose a MAL algorithm and apply it for optimizing online resource allocation in cluster networks. The learning is distributed to each cluster, using local information only and without access to the global system reward. Experimental results are encouraging: our multiagent learning approach performs reasonably well, compared to an optimal solution, and better than a centralized myopic allocation approach in some cases.