Household Electricity Load Prediction With Neural Error Correction
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Solution Overview
Problem
Current load prediction techniques for household electricity loads face challenges due to limited storage of historical data and high randomness and uncertainty in usage patterns, making it difficult to achieve accurate and cost-effective predictions.
Innovation Solution
A method and apparatus for predicting household electricity load that constructs a model comprising long-term stable, medium-term fluctuating, and short-term random load components, and uses a neural network to dynamically correct calculated load values, thereby obtaining predicted load values without the need for extensive historical data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for load prediction, then prediction accuracy is improved, but large amounts of historical data are required for training
Solution Approach 1:
The patent segments the household electricity load into three distinct components: long-term stable load (base load), medium-term fluctuating load (weather-related load), and short-term random load (appliance switching load). Each component is modeled separately using appropriate methods, avoiding the need for deep learning while maintaining prediction accuracy. This segmentation resolves the contradiction by eliminating the requirement for large historical data volumes while preserving prediction precision.
Solution Approach 2:
The patent changes the modeling parameters from requiring extensive historical data to using real-time or recent data with predefined modeling parameters. The long-term stable load uses household-specific parameters, the medium-term load uses weather parameters, and the short-term load uses recent prediction errors. This parameter transformation resolves the contradiction by achieving accurate prediction without deep learning's data hunger.
2Ease of manufacture
If traditional load prediction methods are used, then model training is simplified, but prediction accuracy deteriorates due to limited historical data
Solution Approach 1:
By segmenting the load into three components and applying different modeling strategies to each, the patent achieves both simplicity and accuracy. The long-term stable load uses simple regression, the medium-term load uses weather-based modeling, and the short-term load uses error correction - all simpler than deep learning but collectively more accurate than any single traditional method.
Solution Approach 2:
The patent introduces an error correction mechanism as an intermediary between traditional prediction methods and actual load values. The short-term random load component acts as a mediator that captures prediction deviations and feeds back into the model, improving accuracy without requiring complex training procedures or large historical datasets.
3Measurement precision
If household-specific prediction is implemented, then prediction accuracy is improved, but applicability is reduced due to geographical and household variations
Solution Approach 1:
The patent creates a universal prediction framework that can be applied to any household regardless of location or size. The long-term stable load component uses universal parameters (household size, house area) that can be determined for any household. The medium-term component uses universal weather data, and the short-term component uses universal error correction methodology. This universality resolves the contradiction by enabling accurate prediction across diverse geographical and household conditions.
Solution Approach 2:
While maintaining overall universality, the patent incorporates local quality by allowing each household to have its own long-term stable load characteristics based on its specific attributes (size, occupancy, location). The model adapts to local household conditions while using universal modeling approaches, resolving the contradiction between customization for accuracy and universality for applicability.
Data Source
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AI summary
Provided in the present invention are a method and apparatus for predicting a household electricity load. The method comprises the following steps: constructing a household electricity load model; according to the household electricity load model, acquiring a calculated load value of a unit time period to be subjected to prediction; and correcting the calculated load value of said unit time period by means of a neural network, so as to obtain a predicted load value of said unit time period. The present invention can conveniently and accurately predict a household electricity load, and is relatively low in terms of cost and has a relatively wide application range.