Optimizing and controlling the energy consumption of a building
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current building energy management systems fail to accurately adjust heating and cooling based on local weather forecasts and the specific thermal characteristics of a building, leading to inefficient energy use and discomfort.
Innovation Solution
A system that uses sensors and computing devices to generate thermal response coefficients based on building energy characteristics and weather data, predicting energy needs, and adjusting comfort devices like thermostats and shades to optimize energy consumption while maintaining occupant comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a standard thermostat automatically adjusts temperature based on a predetermined schedule, then the system can operate without manual intervention, but the adjustments are based on incomplete or inaccurate weather information and do not account for the building's 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 building to be prepared in advance for upcoming weather changes, reducing the need for reactive adjustments and improving energy efficiency while maintaining accurate temperature control.
2Speed
If the thermostat reacts to current weather conditions, then the system responds to immediate needs, 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 pre-heating and pre-cooling actions before the actual temperature changes occur. This eliminates the reactive delay inherent in traditional thermostats that only respond to current conditions, allowing the building to be prepared in advance for upcoming weather changes.
3Adaptability or versatility
If manual temperature adjustment is used to account for weather conditions, then occupant preferences can be considered, but the system requires continuous manual intervention and does not automatically adapt
Solution Approach 1:
The system automatically adjusts temperature settings based on forecasted weather conditions and the building's thermal characteristics without requiring manual intervention. It self-adapts to maintain optimal comfort levels by using the building's own thermal mass and insulation properties to perform pre-heating and pre-cooling operations.
4Loss of energy
If pre-heating and pre-cooling are implemented based on forecast weather, then energy efficiency is improved, but the system requires access to accurate forecasted weather data and building thermal characteristics
Solution Approach 1:
The system uses forecasted weather data to perform pre-heating and pre-cooling operations in advance of actual temperature changes. By leveraging the building's thermal mass and insulation properties, it can store thermal energy during periods of favorable conditions and release it when needed, significantly reducing overall energy consumption for heating and cooling.
Data Source
AI summary
Described herein are methods and systems, including computer program products, for determining a load control schedule for energy control devices using a load shifting optimization model and applying the load control schedule to adjust the energy control devices. A server receives thermodynamic models, energy price data and energy load forecast data. The server generates price probability distribution curves based upon the price data and load probability distribution curves based upon the load forecast data. The server executes a load shifting optimization model to determine a profit probability distribution curve for demand response decision rules. The server determines a profit curve that has an optimal profit value. The server generates a load control schedule based upon the optimal profit curve and generates operational parameters for energy control devices using the load control schedule. The server transmits the operational parameters to the energy control devices to adjust operational parameters.