An Adaptive Standardization Methodology for Improving Day-Ahead Electricity Price Forecasting

Centrala begrepp

The authors propose an adaptive standardization model to improve the forecasting of Day-Ahead electricity prices by ensuring comparability across all time points in the series and mitigating the effects of dataset shifts.

Sammanfattning

The key highlights and insights of the content are:

The authors propose a new model for Day-Ahead electricity price forecasting (EPF) that performs an adaptive standardization of the price series to ensure comparability across all time points. This helps the model effectively respond to evolving market conditions.

The authors provide access to two new datasets spanning from 2019 to 2023, which offer a more realistic representation of the current electricity market context compared to conventional datasets.

The proposed approach represents a significant statistical improvement over state-of-the-art models, including both classical statistical methods and advanced machine learning techniques. This progress has not been observed since 2016.

The authors investigate four distinct electricity markets, including the novel datasets, and demonstrate that the adaptive standardization model outperforms other methods across all markets, highlighting its robustness to different scenarios.

The authors discuss the importance of addressing outliers in the data, as they can significantly impact the performance of the adaptive standardization approach. They propose a preprocessing step to mitigate the influence of large price spikes.

The authors emphasize the need for a dynamic evaluation approach, examining the month-to-month performance of the models, to better assess their ability to adapt to changing market conditions.

Statistik

The Spanish Day-Ahead electricity market has experienced significant changes in price dynamics, with a discernible alteration towards the end of 2021.
The authors provide access to two new datasets spanning from January 1st, 2019 to May 31st, 2023 for the OMIE-SP (Spain) and EPEX-DE (Germany) markets.
The authors also consider the EPEX-FR (France) and EPEX-BE (Belgium) markets, with datasets from January 9th, 2011 to December 31st, 2016.

Citat

"The focus of the model is to perform an adaptive standardisation of the series to be predicted, making all the time points of the series comparable to each other."
"The proposed model improves the results of the state-of-the-art, with statistical evidence, in four different markets and in two periods characterised by completely different behaviour, highlighting the robustness to different scenarios."

Viktiga insikter från

An adaptive standardisation methodology for Day-Ahead electricity price forecasting

by Carl... på arxiv.org 04-26-2024

https://arxiv.org/pdf/2311.02610.pdf

Djupare frågor

How can the proposed adaptive standardization methodology be extended to other time series forecasting problems beyond electricity price forecasting

The proposed adaptive standardization methodology for electricity price forecasting can be extended to other time series forecasting problems by adapting the approach to suit the specific characteristics of the new domain. Here are some ways in which this extension can be achieved:

Feature Engineering: Identify relevant features in the new time series data that can be standardized adaptively to improve comparability and model performance. This may involve understanding the underlying patterns and dynamics of the data to determine the most effective transformation approach.

Model Selection: Choose appropriate forecasting models that can leverage the adaptive standardization technique effectively. Different models may require different preprocessing steps, so it is essential to select models that align with the adaptive standardization methodology.

Parameter Estimation: Develop methods to estimate the parameters for adaptive standardization based on the specific requirements of the new time series data. This may involve adjusting the window size, regularization parameters, or other hyperparameters to optimize the standardization process.

Evaluation Metrics: Define evaluation metrics that are relevant to the new domain and assess the performance of the adaptive standardization approach. This may include comparing the results with baseline models, conducting statistical tests, and analyzing the impact of the standardization on forecasting accuracy.

By customizing the adaptive standardization methodology to suit the characteristics of different time series forecasting problems, it can be effectively applied beyond electricity price forecasting to improve the accuracy and robustness of predictions.

What are the potential limitations or drawbacks of the adaptive standardization approach, and how can they be addressed

While the adaptive standardization approach offers several benefits for time series forecasting, there are potential limitations and drawbacks that need to be considered:

Sensitivity to Outliers: The methodology may be sensitive to outliers in the data, leading to skewed standardization results. Outliers can significantly impact the estimation of parameters, affecting the overall performance of the model.

Computational Complexity: Implementing adaptive standardization with rolling windows and parameter estimation can increase the computational complexity of the forecasting process. This may require additional computational resources and time for model training and evaluation.

Model Interpretability: The adaptive standardization process may introduce additional complexity to the forecasting models, making it challenging to interpret the results and understand the underlying relationships between variables.

To address these limitations, techniques such as outlier detection and treatment, optimization of parameter estimation methods, and simplification of the standardization process can be implemented. Additionally, conducting sensitivity analyses and robustness checks can help identify and mitigate potential drawbacks of the adaptive standardization approach.

How can the insights from this study on the importance of dynamic evaluation and outlier treatment be applied to improve forecasting in other domains with non-stationary and volatile time series

The insights from this study on dynamic evaluation and outlier treatment can be applied to improve forecasting in other domains with non-stationary and volatile time series in the following ways:

Dynamic Evaluation: Implementing a dynamic evaluation approach allows for the continuous monitoring of model performance over time. By analyzing month-to-month variations in forecasting accuracy, potential issues and anomalies can be identified and addressed promptly, leading to more reliable predictions.

Outlier Treatment: Developing robust outlier detection and treatment strategies can help mitigate the impact of anomalies in the data on forecasting models. By preprocessing the data to handle outliers effectively, the models can produce more accurate and stable predictions, especially in volatile time series.

Adaptive Standardization: Applying adaptive standardization techniques to other forecasting domains can enhance the comparability of time series data and improve model performance. By customizing the standardization process to suit the specific characteristics of the data, models can better capture underlying patterns and make more accurate predictions.

Overall, incorporating dynamic evaluation, outlier treatment, and adaptive standardization methodologies can enhance forecasting accuracy and robustness in diverse domains with non-stationary and volatile time series data.