Method and system for adaptively switching prediction strategies optimizing time-variant energy consumption of built environment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The integration of renewable energy sources into energy management systems poses challenges due to their intermittent nature, leading to fluctuations in energy demand and consumption, which complicates investment, power generation, and pricing, especially when traditional energy storage solutions like batteries face limitations in sizing and load matching.
Innovation Solution
A computer-implemented method and system that analyzes statistical data from energy consuming devices, user occupancy, energy storage and supply means, environmental sensors, and pricing models to adaptively switch prediction strategies, optimizing energy demand and consumption by shifting and scheduling device operations and integrating energy storage and supply means to reduce peak demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional energy storage solutions like batteries are used to store energy when power production is excessive, then energy can be released when demands exceed power production output, but there are limitations due to sizing requirements, specific load profiles and other attributes which must be matched very carefully
Solution Approach 1:
The patent implements dynamic switching between multiple prediction strategies (statistical models, machine learning models, deep learning models) based on real-time renewable energy generation patterns and demand characteristics. This dynamic adaptation allows the system to optimize energy storage and consumption without requiring perfectly matched static storage capacities, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The system changes operational parameters by adjusting prediction horizons, model selection, and control strategies based on varying renewable energy availability and demand conditions. This parameter adaptation enables effective energy management with flexible storage sizing, reducing the need for carefully matched storage configurations while maintaining reliability.
2Object-generated harmful factors
If renewable energy sources are used to generate electricity, then carbon emissions are reduced, but power production becomes intermittent and cannot be controlled actively by humans, creating challenges for energy utilities to make investments and establish pricing
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor renewable energy generation, demand patterns, and pricing signals. This feedback enables the system to adapt prediction strategies and control actions in real-time, allowing renewable energy systems to respond to varying conditions and establish dynamic pricing models that reflect actual supply and demand, thereby improving adaptability while maintaining low carbon emissions.
Solution Approach 2:
The system performs preliminary actions by predicting future energy generation and demand using multiple statistical and machine learning models. These advance predictions allow energy utilities to make informed investment decisions and establish pricing strategies beforehand, compensating for the intermittent nature of renewable energy and improving production adaptability without increasing carbon emissions.
3Loss of energy
If energy storage means are deployed to store energy when power production is excessive, then energy can be released when demands exceed power production output, but sizing requirements and specific load profiles must be matched very carefully to provide feasible economic returns
Solution Approach 1:
The patent employs dynamic prediction and control strategies that adapt to varying renewable energy generation and demand patterns. This dynamic approach optimizes energy storage and release operations without requiring precisely matched static storage configurations, reducing both energy waste and the complexity of storage system configuration while maintaining economic feasibility.
Data Source
AI summary
A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.