Systems and methods for modeling energy consumption and creating demand response strategies using learning-based approaches
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Solution Overview
Problem
Existing demand response strategies for residential HVAC systems are inefficient due to simplified energy consumption models that fail to accurately capture the complex thermal behavior of homes under varying weather conditions and dynamic pricing environments, leading to suboptimal energy usage and costs.
Innovation Solution
A learning-based approach using actual energy usage data and weather forecasts to model energy consumption, allowing for the development of more effective demand response strategies that adjust thermostat settings and appliance operation to minimize energy costs, incorporating neural networks, regression models, and optimization techniques to optimize energy usage based on real-time pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplified energy consumption models are used for HVAC systems, then device complexity is reduced, but manufacturing precision (model accuracy) deteriorates
Solution Approach 1:
The patent transforms the energy consumption model from a static simplified form to a dynamic adaptive form by introducing learning algorithms that continuously update model parameters based on actual energy usage data and weather conditions, resolving the contradiction between model simplicity and accuracy
Solution Approach 2:
The patent replaces traditional physics-based thermal models with data-driven machine learning models (neural networks, regression models) that learn energy consumption patterns from historical data, achieving higher accuracy without requiring complex physical calculations
2Ease of operation
If traditional fixed thermostat settings are used, then ease of operation is improved, but energy consumption increases under dynamic pricing conditions
Solution Approach 1:
The patent implements self-service through automated demand response strategies where the system automatically adjusts thermostat settings based on learned energy consumption patterns, weather forecasts, and electricity pricing signals without requiring manual user intervention, maintaining simplicity while optimizing energy usage
Solution Approach 2:
The patent incorporates feedback mechanisms where actual energy consumption data and temperature measurements are continuously fed back to the learning model, which then refines future thermostat control decisions to minimize energy consumption while maintaining comfort
3Measurement precision
If learning-based approaches are implemented to model energy consumption, then measurement precision of energy usage is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex learning-based system into distinct functional modules: data collection module, learning model module (neural network/regression), prediction module, and control module, making the overall system more manageable and implementable while maintaining high prediction accuracy
Data Source
AI summary
According to various implementations, a demand response (DR) strategy system is described that can effectively model the HVAC energy consumption of a house using a learning based approach that is based on actual energy usage data collected over a period of days. This modeled energy consumption may be used with day-ahead energy pricing and the weather forecast for the location of the house to develop a DR strategy that is more effective than prior DR strategies. In addition, a computational experiment system is described that generates DR strategies based on various energy consumption models and simulated energy usage data for the house and compares the cost effectiveness and energy usage of the generated DR strategies.