מושגי ליבה
This paper proposes a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs that effectively handles missing data through iterative imputation and end-to-end training.
תקציר
The paper introduces a nonparametric end-to-end approach for probabilistic forecasting of distributed renewable generation outputs that addresses the challenge of missing data.
Key highlights:
- The method employs a nonparametric probabilistic forecast model using LSTM to model the probability distributions of distributed renewable generation outputs.
- An end-to-end training process is designed that includes missing data imputation through iterative imputation and iterative loss-based training procedures.
- This two-step modeling approach combines the strengths of the nonparametric method with the end-to-end approach, demonstrating exceptional capabilities in probabilistic forecasting while effectively handling missing values.
- Simulation results confirm the superior performance of the proposed approach compared to existing alternatives like linear interpolation, k-nearest neighbors, and autoregressive LSTM.
- The nonparametric end-to-end method outperforms its parametric counterpart and traditional statistical imputation techniques in terms of reliability, sharpness, and overall skill score.
- The synergistic combination of the nonparametric approach, end-to-end structure, and deep learning enables the proposed method to achieve highly competitive performance in probabilistic forecasting under missing data scenarios.
סטטיסטיקה
The global energy transition has accelerated the development of distributed generation systems.
Distributed generation systems exhibit high levels of randomness due to factors like weather conditions, load fluctuations, and equipment reliability.
Randomness leads to uncertainty and complexity in system operation, making it difficult to accurately predict the future operational state of the system.
Accurate characterization of the stochastic nature of distributed generation requires effective missing data handling techniques, as missing values may result from sensor failures, data collection errors, etc.
ציטוטים
"To effectively characterize the stochastic nature of distributed generation systems for better prediction, management, and optimization of system operations, various techniques are employed."
"Characterizing the stochastic nature of distributed generation systems is crucial for achieving stable operation and optimal management."
"The existence of missing values may lead to incomplete data, thereby affecting the accuracy of subsequent data analysis and prediction models."