Energy Management System with Real-Time Feedback for Operatives
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Solution Overview
Problem
Operative workers in high-touch and on-demand services lack real-time feedback on energy use, making it difficult for them to reduce energy consumption while meeting business goals, as existing energy management systems are not integrated into their daily workflows and often provide overwhelming or irrelevant information.
Innovation Solution
An energy management system that provides near real-time submetering and feedback using discrete time intervals and energy budgets, allowing for customizable and manageable feedback to operatives, with statistical modeling and machine learning to adapt to changing circumstances, and user interfaces that convey energy use data in a way that is easy to understand and act upon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional energy management systems are used to monitor and report energy consumption, then energy use data can be collected and analyzed, but operative workers cannot access real-time feedback in their daily workflows
Solution Approach 1:
The system segments energy feedback delivery by worker role, task type, and time interval. Different workers receive customized feedback based on their specific responsibilities and the devices they operate, rather than a unified comprehensive report system. This segmentation enables workers to access relevant energy information without being overwhelmed by unnecessary data.
Solution Approach 2:
The system introduces an intermediary feedback layer between the energy management platform and workers. This intermediary translates complex energy data into simple, actionable cues delivered through familiar communication channels (email, text messages, dashboard notifications), making energy information accessible without requiring workers to interact with complex monitoring systems.
2Measurement precision
If comprehensive energy management data is provided to workers, then energy consumption can be monitored, but workers receive overwhelming or irrelevant information that distracts from their tasks
Solution Approach 1:
The system applies local quality by tailoring feedback content, detail level, and delivery method to each worker's specific role, responsibilities, and task context. Instead of providing uniform comprehensive data to all workers, each receives customized information relevant to their energy consumption patterns and operational scope.
Solution Approach 2:
The system implements partial action by providing only the specific energy feedback information needed for each worker's immediate tasks, rather than complete comprehensive data. Feedback is delivered at appropriate intervals and in appropriate detail levels, avoiding information overload while maintaining sufficient precision for energy management.
3Productivity
If real-time feedback is provided to workers during their tasks, then energy consumption can be reduced, but the feedback mechanism adds complexity to the energy management system
Solution Approach 1:
The system implements feedback by delivering real-time or near-real-time energy consumption information to workers during their tasks. This feedback includes simple cues about energy use levels, budget status, and actionable recommendations, enabling workers to adjust their behavior and reduce energy consumption without requiring complex system changes.
Solution Approach 2:
The system achieves multi-functionality by delivering feedback through existing communication infrastructure (email systems, text messaging platforms, web dashboards) that workers already use for their daily tasks. This approach provides real-time energy feedback without requiring dedicated specialized hardware or complex new communication channels.
4Adaptability or versatility
If energy management systems are integrated into worker workflows, then real-time feedback can be provided, but the systems require significant customization and configuration
Solution Approach 1:
The system leverages existing universal communication platforms and tools that workers already use for their daily operations. By delivering energy feedback through familiar channels like email, text messages, and web browsers, the system achieves workflow integration without requiring custom-built communication infrastructure or extensive technical configuration.
Solution Approach 2:
The system enables self-service by allowing workers to access energy feedback information through interfaces they already know how to use. Workers can view their energy consumption data, receive alerts, and take action using standard web browsers or mobile devices, eliminating the need for specialized training or complex system configuration.
Data Source
AI summary
An embodiment models and predicts energy consumption and provides recurring and realistic opportunities to reduce energy consumption throughout the work day or process cycle using user interfaces to convey positive and negative feedback in a controlled manner; and user experience, that reward positive changes with increased positive feedback and reduced negative feedback. Energy consumption of categories of appliances, devices, and equipment is considered a random variable. Using archived energy data, business data, and other related data, statistical modeling is used to create inverse cumulative probability distribution functions. An energy budget (consumption prediction) is computed so that it meets a probability p of the budget being exceeded during a given interval. When the budget is exceeded the feedback is negative, otherwise feedback is positive. Each budget is computed as the value b of the random variable such that the probability that the random variable will be less than or equal to b is 1-p.