Building Energy Control Using Net Zero Consumption Trajectories
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Solution Overview
Problem
Existing building management systems face challenges in optimally controlling energy consumption to achieve net zero energy status over a desired time period, particularly due to asynchronous energy production and consumption and the need to adapt to changing conditions.
Innovation Solution
A method and system that utilize predictive optimization and curtailment actions to balance energy production and consumption, including the use of green energy sources and digital twins to manage HVAC and lighting systems, with a user dashboard for visualization and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If predictive optimization and curtailment actions are implemented to balance energy production and consumption, then net zero energy status is achieved, but system complexity increases
Solution Approach 1:
The system performs predictive optimization in advance to determine the net energy trajectory and required curtailment actions before the time period begins. By calculating the optimal energy balance strategy beforehand and breaking it down into subperiod targets, the system reduces real-time computational complexity while achieving net zero energy status.
Solution Approach 2:
The control system divides the time period into multiple subperiods with individual net consumption targets. This segmentation allows the complex optimization problem to be solved in manageable increments, reducing overall system complexity while maintaining the ability to achieve net zero energy across the entire period.
2Productivity
If curtailment actions are dynamically adjusted to match energy production, then energy balance is optimized, but control difficulty increases
Solution Approach 1:
The system pre-calculates the net energy trajectory and determines optimal curtailment actions for each subperiod before execution. This preliminary planning simplifies real-time control operations while maintaining high energy balance efficiency, as the complex optimization decisions have already been made in advance.
Solution Approach 2:
The system dynamically adjusts curtailment actions across different subperiods based on predicted energy production and consumption patterns. By making the control strategy adaptable to changing conditions while maintaining a pre-established framework, the system achieves optimized energy balance without excessive control difficulty.
3Use of energy by moving object
If net consumption targets are set for multiple subperiods, then energy distribution is optimized, but measurement and control precision requirements increase
Solution Approach 1:
The system divides the overall energy optimization problem into multiple subperiods with individual net consumption targets. This segmentation improves energy distribution efficiency by allowing tailored optimization for different time periods, while the modular structure helps manage measurement precision requirements through incremental verification at each subperiod boundary.
Data Source
AI summary
A method includes providing, by processing circuitry, a net resource consumption trajectory including net resource consumption targets for one or more subperiods of a time period. Each net resource consumption target indicates a target difference between resource consumption and resource production or offset for a subperiod of the one or more subperiods. The method includes generating, by the processing circuitry and for a subperiod of the one or more subperiods, a set of actions predicted to achieve the net resource consumption target for the subperiod. The method includes implementing, by the processing circuitry, the set of actions.