Context-Aware Forecast Model Repository for Energy Demand
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing energy forecasting models face challenges in accurately predicting future electricity consumption and production due to the variability of renewable energy sources and customer demands, which can lead to unstable electricity grids and inefficiencies in energy supply management.
Innovation Solution
A computer-implemented method using a Context-Aware Forecast Model Repository (CFMR) organized as a binary search tree to retrieve and optimize forecast model parameters based on context vectors, allowing for real-time adaptation to changing energy demand patterns and context drifts, such as abrupt, persistent, and cyclic changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If forecast models use historical electricity time series data, then forecasting accuracy can be improved, but the models cannot adapt quickly enough to changing energy demand patterns and context drifts
Solution Approach 1:
The system dynamically selects and optimizes forecast model parameters based on current context vectors that capture changing energy demand patterns. The context-aware parameter selection mechanism allows the model to adapt its behavior to different operational conditions (e.g., peak demand, off-peak, seasonal variations) while maintaining historical data foundations, thus resolving the contradiction between using historical data for accuracy and adapting to changes.
Solution Approach 2:
The patent changes model parameters based on context vectors derived from current energy demand patterns. By selecting different parameter sets from the repository based on contextual conditions and optimizing them in real-time, the system maintains forecasting accuracy while adapting to changing patterns, effectively resolving the contradiction between historical data reliance and adaptability.
2Adaptability or versatility
If the system retrieves and optimizes forecast model parameters in real-time, then adaptability to changing energy demands is improved, but computational complexity and processing time increase
Solution Approach 1:
The system pre-stores multiple forecast model parameters in a repository organized by context attributes, allowing rapid retrieval during real-time operations. This preliminary organization of parameters avoids the need to compute everything from scratch, reducing computational complexity while maintaining real-time adaptability.
Solution Approach 2:
The system copies and retrieves pre-computed parameter vectors from the repository based on context matching, rather than re-computing optimal parameters from scratch. This copying approach significantly reduces computational complexity while enabling real-time adaptation to changing energy demand patterns.
3Productivity
If the system uses a repository to store and retrieve parameter vectors, then parameter optimization efficiency is improved, but the system complexity and data management requirements increase
Solution Approach 1:
The parameter repository is segmented and organized by context attributes (e.g., time of day, day of week, seasonal factors), allowing efficient retrieval of relevant parameters through structured queries. This segmentation improves productivity by enabling fast, targeted parameter retrieval while managing complexity through systematic organization rather than monolithic storage.
Data Source
AI summary
Methods, systems, and computer-readable storage media for providing at least one parameter for use with a forecast model. Implementations include actions of receiving a first context vector, the first context vector including a plurality of context attributes that describe a first context, retrieving a first parameter vector from a repository based on the first context vector, the repository electronically storing a plurality of parameter vector, each parameter vector being associated with a respective context and including one or more parameters, parameterizing the forecast model based on parameters provided in the first parameter vector to provide a parameterized forecast model, optimizing the parameterized forecast model to provide an optimized forecast model, and forecasting one or more values using the optimized forecast model.