Building Automation Energy Prediction for Demand Response Control

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Solution Overview

Problem

Building automation systems lack effective methods to predict and manage energy consumption and curtailment strategies, leading to inefficiencies in reducing energy intensity while maintaining occupant comfort and manufacturing throughput.

Innovation Solution

A building automation system that utilizes historical data and machine learning to predict the outcomes of energy curtailment actions, allowing users to schedule participation in demand response events and apply rules-based strategies to optimize energy reduction while minimizing disruptions to comfort and processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If building automation systems implement energy curtailment strategies to reduce energy intensity, then energy consumption is reduced, but occupant comfort and manufacturing throughput may be degraded

Engineering Contradiction:

Improveenergy consumptionVSAvoidoccupant comfort and manufacturing throughput

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system performs preliminary actions by predicting the outcomes of proposed curtailment actions before implementation. Machine learning models forecast energy consumption, occupant comfort impact, and manufacturing throughput degradation, allowing operators to evaluate multiple scenarios and select optimal curtailment strategies that minimize energy loss while maintaining reliability thresholds.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If building automation systems use rule-based strategies for demand response events, then ease of operation is improved, but adaptability to varying conditions is reduced

Engineering Contradiction:

Improveoperational simplicityVSAvoidadaptability to varying conditions

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system transitions from static rule-based strategies to dynamic machine learning models that continuously adapt to varying conditions. The models learn from historical data and real-time inputs, automatically adjusting curtailment recommendations based on changing occupancy patterns, weather conditions, and manufacturing requirements, thereby maintaining ease of operation while significantly improving adaptability.

Inventive Principle:

Principle #15Dynamics

3Productivity

If building automation systems implement automated decision-making using machine learning, then productivity is improved, but device complexity increases

Engineering Contradiction:

Improveenergy management efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system introduces an intermediary machine learning layer between data collection and decision-making. The machine learning models process historical and real-time data, translating complex patterns into actionable curtailment recommendations. This intermediary handles the computational complexity internally, allowing the user interface to remain simple while achieving high productivity through automated, data-driven decision-making.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250015591A1Energy control and predictive model of building automation systems

Publication Date: 2025.01.09 TRANE INTERNATIONAL INC

AI summary

Method and systems for controlling electrical loads of a power source from a load facility are provided. The method includes determining a set of electrical loads, determining a set of conditions, predicting energy consumption using a machine learning model based on the set of electrical loads and the set of conditions, visualizing the energy consumption on a display device, and controlling the set of electrical loads under the set of conditions.