Dynamic Monthly Energy Forecasting for Real-Time Facility Management
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Solution Overview
Problem
Existing energy management systems fail to account for dynamically changing environmental, business, and operational conditions, leading to inaccurate energy usage predictions and costs.
Innovation Solution
A system utilizing machine learning to dynamically gather and analyze data from various sources, including equipment, environmental sensors, and operational systems, to adjust energy usage predictions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed Average Monthly Usage (AMU) is used for energy management, then equipment degradation can be addressed, but the system cannot account for dynamic changes in facility usage patterns or energy costs
Solution Approach 1:
The system transitions from a fixed AMU to a dynamic AMU that automatically adjusts based on real-time facility usage patterns, environmental conditions, and operational data. The dynamic AMU recalculates monthly thresholds adaptively, allowing the energy management system to respond to changing business needs, seasonal variations, and equipment performance degradation without manual intervention.
2Loss of energy
If third-party vendors implement energy management programs, then energy savings can be achieved through equipment upgrades, but the system lacks integration with actual facility operational data
Solution Approach 1:
The system implements continuous feedback loops where actual energy consumption data, facility usage patterns, and operational metrics are constantly monitored and fed back into the dynamic AMU calculation engine. This feedback mechanism allows the system to learn from actual performance, adjust predictions accordingly, and provide actionable insights that are grounded in real facility operations rather than theoretical models.
3Productivity
If energy management systems use general facility data, then broad energy savings can be achieved, but predictions do not account for specific facility variables and operational nuances
Solution Approach 1:
The system applies local quality by tailoring energy management parameters and dynamic AMU calculations to each specific facility's unique characteristics, equipment inventory, operational patterns, and environmental conditions. Rather than applying uniform management strategies across all facilities, the system customizes its approach based on facility-specific data, ensuring predictions and recommendations are precisely calibrated to local needs and conditions.
Data Source
AI summary
A system and method of dynamically calculating average monthly energy use through a system of weights attributed to business, environmental, and operational variables. The system applies machine learning to improve upon its estimations by learning the correlations of equipment and operational factors to the overall equipment use.