Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs with Missing Data Imputation

แนวคิดหลัก

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.

บทคัดย่อ

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.

สถิติ

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.

คำพูด

"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."

ข้อมูลเชิงลึกที่สำคัญจาก

Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation

by Minghui Chen... ที่ arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00729.pdf

สอบถามเพิ่มเติม

How can the proposed end-to-end framework be extended to handle other types of missing data mechanisms beyond the MCAR assumption

To extend the proposed end-to-end framework to handle missing data mechanisms beyond the Missing Completely at Random (MCAR) assumption, several modifications and enhancements can be implemented. One approach is to incorporate more sophisticated imputation techniques that can adapt to different missing data patterns. For example, techniques like Multiple Imputation by Chained Equations (MICE) or Generative Adversarial Networks (GANs) can be integrated into the framework to handle missing data mechanisms that follow Missing at Random (MAR) or Not Missing at Random (NMAR) patterns. These methods can capture the underlying relationships in the data and generate more accurate imputations. Additionally, ensemble methods that combine multiple imputation strategies can be employed to improve the robustness of the imputation process. By enhancing the imputation step with advanced techniques, the end-to-end framework can effectively handle a wider range of missing data mechanisms.

What are the potential limitations of the nonparametric approach in modeling the probability distributions of distributed renewable generation outputs, and how can these be addressed

While the nonparametric approach offers flexibility and does not rely on specific distribution assumptions, it may have limitations in capturing complex dependencies and tail behaviors in the probability distributions of distributed renewable generation outputs. One potential limitation is the scalability of the model with increasing data complexity and dimensionality. To address this, techniques such as dimensionality reduction or feature selection can be applied to streamline the modeling process and improve computational efficiency. Another limitation is the interpretability of the nonparametric model, as it may not provide explicit insights into the underlying factors driving the probabilistic forecasts. To mitigate this, post hoc analysis methods like sensitivity analysis or feature importance ranking can be employed to understand the impact of input variables on the forecasted distributions. By addressing these limitations through appropriate techniques, the nonparametric approach can be enhanced to provide more accurate and interpretable modeling of distributed renewable generation outputs.

Given the importance of distributed generation in the energy transition, how can the insights from this work be applied to optimize the integration and management of distributed renewable resources in the broader power system context

The insights from this work can be leveraged to optimize the integration and management of distributed renewable resources in the broader power system context by informing decision-making processes and system planning strategies. The probabilistic forecasting capabilities developed in this study can be utilized to enhance the reliability and stability of power systems with high penetrations of distributed generation. By incorporating probabilistic forecasts into energy management systems, operators can make more informed decisions regarding generation scheduling, grid balancing, and resource allocation. Additionally, the end-to-end framework's ability to handle missing data can improve the quality of data-driven models used for system optimization and control. Furthermore, the nonparametric nature of the approach allows for flexibility in modeling diverse renewable generation sources, enabling better adaptation to the dynamic and uncertain nature of distributed energy systems. Overall, applying the insights from this research can lead to more efficient and resilient integration of distributed renewable resources into the broader power system landscape.