Reinforcement Learning Forecasting for Battery Error Compensation
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Solution Overview
Problem
Existing renewable energy forecasting methods, using objective functions like MAE and MSE, fail to consider the sign of forecasting errors, which are critical for battery state management, leading to ineffective error compensation between forecasted and dispatch values sent to the power grid.
Innovation Solution
A renewable energy error compensable forecasting method using reinforcement learning, where a policy πθ is modeled to minimize the sum of an error function ƒt+1D, allowing for compensable errors by charging or discharging the battery based on under- or over-forecasting scenarios, thereby interacting with the battery to adjust forecasted values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MAE or MSE is used as the objective function to reduce forecasting error, then the error between measured value and forecasted value is reduced, but the sign of the forecasting error is not considered, making it impossible to determine whether over-forecasting or under-forecasting occurs
Solution Approach 1:
The patent segments the forecasting error analysis into two distinct components: over-forecasting error and under-forecasting error. By introducing separate objective functions (loss_over and loss_under) for each type of error, the system can independently control and optimize for each direction of error, thereby preserving the sign information that was previously lost in aggregate metrics like MAE or MSE.
2Measurement precision
If the goal is to minimize the error between measured value and forecasted value, then forecasting accuracy is improved, but the battery cannot effectively compensate for errors because the sign information is lost
Solution Approach 1:
The patent implements a feedback mechanism where the forecasting model receives explicit feedback about the sign and magnitude of errors through separate loss functions. The reinforcement learning agent observes the battery state and error type, then adjusts future forecasts accordingly. This feedback loop enables the system to learn from past over- or under-forecasting events and improve compensation strategies, thereby enhancing reliability while maintaining accuracy.
3Device complexity
If conventional forecasting methods are used without considering battery state, then the forecasting process is simple, but the dispatched error between forecasted value and dispatch value cannot be minimized
Solution Approach 1:
The patent introduces dynamics into the forecasting process by making the objective function adaptive based on battery state. Instead of using a static loss function, the system dynamically selects between loss_over and loss_under based on whether the battery is in a charging or discharging state. This dynamic adaptation allows the forecasting model to optimize for the specific compensation scenario at hand, reducing dispatched error without requiring complex manual intervention.
4Ease of manufacture
If the forecasting model only focuses on reducing overall error magnitude, then the model training is straightforward, but the model cannot distinguish between over-forecasting and under-forecasting scenarios
Solution Approach 1:
The patent applies asymmetry by treating over-forecasting and under-forecasting as fundamentally different problems requiring different loss functions. The loss_over function penalizes over-forecasting scenarios while loss_under penalizes under-forecasting scenarios. This asymmetric treatment allows the model to develop specialized understanding of each error type, improving adaptability while maintaining training simplicity through modular loss function design.
Data Source
AI summary
A renewable energy error compensable forecasting method using a battery is provided. The method may include modeling a policy πθ with a parameter θ, by using a reinforcement learning algorithm in which a state st of an agent in a time zone t and, a reward rt+1 in a time zone t+1 and a next state st+1 with respect to an action at taken by the agent are determined; determining the parameter θ capable of minimizing a sum of an error function ƒt+1D; and generating a compensable error with the battery using a model including the parameter θ.