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.

Boliden Tara Mines Ltd. consumed 184.7 GWh of electricity in 2014, equating to over 1% of the national demand of Ireland or approximately 35,000 homes. Ireland’s industrial electricity prices, at an average of 13 c/KWh in 2014, are amongst the most expensive in Europe. Cost effective electricity procurement is ever more pressing for businesses to remain competitive. In parallel, the proliferation of intelligent devices has led to the industrial Internet of Things paradigm becoming mainstream. As more and more devices become equipped with network connectivity, smart metering is fast becoming a means of giving energy users access to a rich array of consumption data. These modern sensor networks have facilitated the development of applications to process, analyse, and react to continuous data streams in real-time. Subsequently, future procurement and consumption decisions can be informed by a highly detailed evaluation of energy usage. With these considerations in mind, this paper uses variable energy prices from Ireland’s Single Electricity Market, along with smart meter sensor data, to simulate the scheduling of an industrial-sized underground pump station in Tara Mines. The objective is to reduce the overall energy costs whilst still functioning within the system’s operational constraints. An evaluation using real-world electricity prices and detailed sensor data for 2014 demonstrates significant savings of up to 10.72% over the year compared to the existing control systems.