Battery-Grid Power Forecasting with Time-Horizon Logic Switching
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Solution Overview
Problem
The accuracy of predicting power suppliable to a power grid decreases with the length of time from the present to the future point of transaction, making it difficult to determine bids in power transaction markets, especially for longer durations like one month or one year ahead.
Innovation Solution
A power calculation apparatus and method that predicts power suppliable and demandable between a battery and a power grid, using a microprocessor to switch calculation logic based on the duration from the present time to a predetermined time, incorporating state information and statistical data to improve prediction accuracy across varying time frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single prediction method is used for all time durations, then the system is simple to operate, but prediction accuracy deteriorates for long-term forecasts
Solution Approach 1:
The prediction system is segmented into multiple calculation logics based on time duration: short-term prediction logic (for immediate future), medium-term prediction logic (for intermediate future), and long-term prediction logic (for distant future). Each logic is optimized for its specific time range, with short-term using real-time battery state data and long-term using historical patterns and statistical methods, thereby maintaining high accuracy across all durations without excessive overall complexity
Solution Approach 2:
The system dynamically selects and switches between different calculation logics based on the prediction time horizon. The microprocessor automatically chooses the appropriate logic (short-term, medium-term, or long-term) depending on the requested prediction duration, allowing the system to adapt its complexity level to the specific prediction task rather than using a fixed complex or simple approach for all cases
2Adaptability or versatility
If prediction duration is extended to far future, then bid opportunities increase in power transaction market, but prediction accuracy decreases
Solution Approach 1:
The system changes key parameters of the prediction approach based on time duration: for short-term predictions, it uses real-time battery state of charge and power rate data; for medium-term, it incorporates daily usage patterns; for long-term predictions, it relies on historical statistical data and seasonal trends. This parameter adaptation allows the system to maintain reasonable accuracy across diverse time horizons from hours to months ahead
Solution Approach 2:
The system introduces intermediate prediction stages with different levels of detail: immediate short-term predictions based on current state, medium-term predictions based on typical usage patterns, and long-term predictions based on historical statistics. These intermediate stages act as mediators that bridge the gap between immediate battery state and distant future requirements, enabling flexible bid opportunities at multiple time scales
3Measurement precision
If detailed calculation logic is used for short-term prediction, then accuracy is high, but computational resources are consumed
Solution Approach 1:
The computational complexity is dynamically adjusted based on prediction duration. For short-term predictions where high accuracy is critical, the system employs detailed calculation logic analyzing real-time battery state of charge, power rates, and immediate usage patterns. For long-term predictions where absolute precision is less critical, it switches to lighter statistical methods using historical averages and trends, thereby optimizing energy consumption while maintaining sufficient accuracy for each time horizon
Data Source
AI summary
A power calculation apparatus calculates an amount of power suppliable and demandable between a battery and a power grid. The power calculation apparatus includes a microprocessor and a memory connected to the microprocessor. The microprocessor is configured to perform: predicting the amount of power suppliable and demandable between the battery and the power grid at a predetermined point in time to calculate a prediction value of the amount of power suppliable and demandable; and switching a calculation logic to be applied to a calculation of the prediction value, based on a length of duration from a present point in time to the predetermined point in time.