Commercial Building Energy Optimization With Thermal Lag Modeling
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Solution Overview
Problem
Existing energy management systems in commercial buildings are inefficient in predicting and reducing energy use, often resulting in discrepancies between design estimates and actual energy consumption, leading to unnecessary waste.
Innovation Solution
A method that utilizes historical energy consumption data and occupant data to optimize energy usage by adjusting operational practices within the building, focusing on matching plant operation to desired comfort levels without requiring changes to the building or plant infrastructure, using techniques like lag parameter calculation and ongoing monitoring to achieve energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional energy management software simulation tools are used to predict energy use, then design estimates of energy use can be obtained, but the predictive strength is weak when comparing design estimates with actual post-occupation energy consumption
Solution Approach 1:
The system continuously monitors actual energy consumption data from building systems and compares it against predicted values, using this feedback to refine and recalibrate predictive models. This closed-loop approach enables the system to learn from discrepancies between design estimates and actual performance, progressively improving prediction accuracy while identifying specific sources of energy waste for correction.
2Loss of energy
If building plant operation is adjusted to reduce energy use, then energy consumption can be reduced, but occupant comfort levels may be compromised
Solution Approach 1:
The system dynamically adjusts operational parameters of building plant systems based on real-time conditions, using calibrated predictive models to determine optimal settings that minimize energy consumption while maintaining comfort within acceptable ranges. The system modifies parameters such as temperature setpoints, equipment runtime schedules, and system operational modes to achieve energy reduction without compromising occupant comfort below desired levels.
3Loss of energy
If building or plant infrastructure is upgraded to improve energy efficiency, then energy use can be reduced, but the cost and complexity of modifications increases
Solution Approach 1:
The system optimizes energy usage by intelligently controlling and coordinating existing building plant equipment rather than requiring physical upgrades. It achieves energy reductions through software-based optimization that self-adjusts operational parameters, schedules, and control strategies of current systems, eliminating the need for costly infrastructure modifications while still delivering significant energy consumption reductions.
Data Source
AI summary
The invention provides a method for optimizing energy usage in commercial buildings. Energy consumption data is used, along with occupant data, to determine appropriate adjustments in energy, and for ongoing monitoring and reporting of energy savings. According to the inventive method, the building of interest is characterized, including calculation of lag parameters—temperature lag, solar gain lag, solar strength lag, and, in some instances, humidity lag, which inform a thermal energy equation particular to the building of interest. Mechanical heating lag and mechanical cooling lag are used for on-going energy use optimization. An outside temperature index may also be used. The resulting accuracy of the thermal energy equation is over 90% for both heat and chilling input, once the building has been optimized according to the inventive method.