Building Energy Control Using Thermal Response Forecasting
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Solution Overview
Problem
Current building energy management systems fail to accurately adjust heating and cooling based on local weather forecasts and building-specific energy characteristics, leading to inefficient energy use and discomfort.
Innovation Solution
A system that uses sensors and weather data to generate thermal response coefficients, predict energy needs, and adjust comfort devices like thermostats and shades to optimize energy consumption and maintain comfort levels, incorporating features like demand response and occupant preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a standard thermostat is used to regulate heating and cooling, then the system can automatically adjust temperature based on a predetermined schedule, but the adjustments are based on incomplete or inaccurate weather information and do not account for building-specific thermal characteristics
Solution Approach 1:
The system performs pre-heating and pre-cooling of the building based on forecasted weather conditions before the actual temperature changes occur. This allows the HVAC system to be proactive rather than reactive, adjusting temperatures in advance of weather changes to maintain comfort while optimizing energy usage.
Solution Approach 2:
The system dynamically changes temperature setpoints and HVAC operation parameters based on real-time weather data, forecasted conditions, and building-specific thermal response coefficients. These parameters are continuously adjusted to optimize both comfort and energy efficiency.
2Reliability
If the HVAC system responds to current weather conditions, then it can maintain comfort levels, but it cannot perform pre-heating or pre-cooling based on forecast weather conditions
Solution Approach 1:
The system uses forecasted weather data to perform preliminary heating or cooling actions before actual temperature changes occur. This time advance allows the building's thermal mass to absorb or release heat, reducing the need for intensive HVAC operation during peak weather conditions.
Solution Approach 2:
The system dynamically adjusts its control strategy based on the time horizon of weather forecasts, building thermal characteristics, and predicted occupancy patterns. This allows flexible optimization of both comfort and energy usage over different time scales.
3Ease of operation
If manual temperature adjustment is used to account for weather conditions, then occupant preferences can be considered, but the system reacts to current conditions rather than anticipating future needs
Solution Approach 1:
The system automatically performs temperature adjustments without requiring manual occupant intervention. It uses sensor data, weather forecasts, and building models to self-regulate the HVAC system, maintaining comfort while optimizing energy usage based on predicted rather than just current conditions.
Solution Approach 2:
The system continuously monitors actual temperature, humidity, and occupancy conditions, comparing them against predicted values and adjusting operations accordingly. This feedback loop ensures both comfort maintenance and energy optimization based on real-time building response.
4Loss of energy
If pre-heating or pre-cooling is implemented based on forecast weather, then energy efficiency is improved, but the system complexity increases due to need for thermal response modeling
Solution Approach 1:
The system determines building-specific thermal response coefficients that characterize how the building responds to heating and cooling inputs. These coefficients are used to simplify the thermal model and enable efficient calculation of optimal pre-heating and pre-cooling strategies without requiring complex real-time simulations.
Solution Approach 2:
The system performs preliminary calculations of thermal response coefficients and optimal control strategies based on forecasted weather conditions. This advance planning enables energy-efficient pre-heating and pre-cooling operations without requiring complex real-time decision-making during peak weather conditions.
Data Source
AI summary
Described herein are methods and systems, including computer program products, for optimizing and controlling a building's energy consumption and comfort. A computing device receives measurements from a plurality of sensors, at least some of which are located inside the building, where the measurements include temperature readings and comfort characteristics. The computing device generates a set of thermal response coefficients based on energy characteristics of the building, the measurements from the sensors, and weather data associated with the building's location. The computing device predicts an energy response of the building based on the set of thermal response coefficients and forecasted weather. The computing device selects minimal energy requirements of the building based on an energy consumption cost associated with the building and determines energy control points based on the energy response and the minimal energy requirements. The computing device transmits the energy control points to comfort devices in the building.