A white paper from Rockwell Automation details the requirements and outlines the potential efficiency gains that commercial and industrial energy users can achieve by applying an integrated, algorithmic approach to energy management. The paper uses examples from several enterprise energy management (EEM) system implementations to validate this holistic methodology, which leverages real-time and historical data to predict performance trends and evaluate response options.
The white paper, An Algorithmic Approach to Enterprise Energy Management: Developing an Integrated Energy Solution Utilizing Real-time Data Collection and Predictive Modeling Capabilities, outlines the fundamental requirements of an EEM system. These core requirements include connectivity to disparate data sources, energy modeling for real-time benchmarking, analytical capabilities to support ongoing systems commissioning, and accessible visualization of energy usage data.
Energy managers are often inhibited by data gaps, disparate data sources and complicated systems interfaces, making it difficult to effectively analyze real-time energy data and maximize efficiencies, said Jeff Soplop, manager, Energy Solutions, Rockwell Automation. Using an aggregated, algorithmic approach allows energy managers to realize higher visibility into ongoing operations, more quickly identify and isolate potential issues, and employ preemptive action based on upcoming events such as weather, production schedules or energy-price changes.
The white paper, An Algorithmic Approach to Enterprise Energy Management: Developing an Integrated Energy Solution Utilizing Real-time Data Collection and Predictive Modeling Capabilities, outlines the fundamental requirements of an EEM system. These core requirements include connectivity to disparate data sources, energy modeling for real-time benchmarking, analytical capabilities to support ongoing systems commissioning, and accessible visualization of energy usage data.
Energy managers are often inhibited by data gaps, disparate data sources and complicated systems interfaces, making it difficult to effectively analyze real-time energy data and maximize efficiencies, said Jeff Soplop, manager, Energy Solutions, Rockwell Automation. Using an aggregated, algorithmic approach allows energy managers to realize higher visibility into ongoing operations, more quickly identify and isolate potential issues, and employ preemptive action based on upcoming events such as weather, production schedules or energy-price changes.
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