Load Prediction Algorithm for Electrical Load Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting electrical load and lifestyle characteristics in residential settings are not optimal, as they fail to accurately identify and manage peak usage patterns, leading to inefficiencies in energy distribution and demand response programs.
Innovation Solution
A system and method that utilize a plurality of modules, including a record receiving module, load identification module, and comparison module, to analyze electrical energy usage records and property characteristics, applying load prediction algorithms to determine the presence of specific electrical loads and simulate their usage, thereby identifying and characterizing electrical loads and lifestyle patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrical load prediction methods are used, then the system is simple to operate, but the prediction accuracy of electrical loads and lifestyle characteristics is insufficient
Solution Approach 1:
The system segments the electrical load prediction process into multiple specialized modules: a record receiving module for data collection, a load identification module for detecting specific electrical loads, a comparison module for analyzing usage patterns, and a simulation module for projecting future loads. Each module handles a specific aspect of the prediction task, improving overall accuracy while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The load prediction system is designed to identify and characterize multiple types of electrical loads (air conditioners, heaters, water heaters, etc.) and lifestyle characteristics using a unified multi-module architecture. This universal approach allows the same system to handle diverse prediction tasks across different electrical devices and user behavior patterns, improving measurement precision through comprehensive analysis.
2Measurement precision
If detailed analysis of electrical energy usage records is performed to identify specific loads, then prediction accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The comparison module performs preliminary analysis of electrical energy usage records by comparing actual usage patterns against characteristic usage patterns of known electrical loads. This preliminary identification allows the system to quickly filter and categorize loads before more detailed simulation and prediction, reducing overall processing time while maintaining identification accuracy.
Solution Approach 2:
The load identification module extracts specific electrical load signatures from aggregate electrical energy usage records by identifying characteristic patterns (such as timing, duration, and power consumption profiles). This extraction process separates individual load behaviors from the total usage data, enabling accurate identification without requiring exhaustive analysis of every data point.
3Reliability
If the system simulates electrical usage of determined loads to create projected energy load, then the accuracy of demand response programs improves, but the computational complexity increases
Solution Approach 1:
The simulation module focuses computational resources on simulating the electrical usage patterns of specifically identified loads (such as air conditioners, water heaters, or pool pumps) rather than attempting to model all possible electrical devices. This localized simulation approach improves the reliability of demand response predictions for target loads while reducing overall computational complexity by concentrating analysis where it is most needed.
4Adaptability or versatility
If multiple load prediction algorithms are applied to determine different electrical loads, then the comprehensiveness of load characterization improves, but the system complexity and processing requirements increase
Solution Approach 1:
The system divides the electrical load prediction task into multiple specialized algorithms, each designed to identify specific types of electrical loads (e.g., one algorithm for air conditioners, another for water heaters, another for pool pumps). The load identification module selectively applies these segmented algorithms based on the characteristics of the usage patterns being analyzed, achieving comprehensive load characterization while managing algorithm complexity through functional specialization.
Data Source
AI summary
An apparatus, system, and method are disclosed for determining electrical load and lifestyle characteristics. A record receiving module receives an electrical energy usage record for premises for a predefined time period (“record period”), and receives property characteristics for the premises. The property characteristics include physical characteristics for the premises, environmental characteristics for the premises for the record period, and/or lifestyle characteristics of users of the premises. A load identification module selects a load prediction algorithm to determine if a particular type of electrical load is present at the premises. A comparison module applies the load prediction algorithm to the electrical energy usage record for the premises for at least a portion of the record period (“comparison period”) to determine if the particular type of electrical load is present at the premises. The load prediction algorithm uses the property characteristics of the premises during the comparison period.