Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit

Hamilton, Robert I.; Papadopoulos, Panagiotis

doi:10.1109/tpwrs.2023.3248941

Cited by 13 publications

(9 citation statements)

References 38 publications

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“…It is crucial to emphasize that SHAP values can reveal relationships between input features and output outcomes learned from the data, they do not inherently signify or mirror causality. Consequently, operators or domain experts should undertake additional verification using domain knowledge or alternative causal reasoning methods to ascertain the causal effects of the interpretable approach Hamilton and Papadopoulos (2023).…”

Section: The Deep-shap Methods Validationmentioning

confidence: 99%

“…Reference Mitrentsis and Lens (2022) uses the feature importance SHAP value method to explain the decision results of photovoltaic power prediction models. Reference Hamilton and Papadopoulos (2023) adopts the feature importance method to obtain the interpretation of machine learning models for location-specific transient stability assessment.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach

Li,

Liu,

Hong

et al. 2024

Front. Energy Res.

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With the successful application of artificial intelligence technology in various fields, deep reinforcement learning (DRL) algorithms have applied in active corrective control in the power system to improve accuracy and efficiency. However, the “black-box” nature of deep reinforcement learning models reduces their reliability in practical applications, making it difficult for operators to comprehend the decision-making mechanism. process of these models, thus undermining their credibility. In this paper, a DRL model is constructed based on the Markov decision process (MDP) to effectively address active corrective control issues in a 36-bus system. Furthermore, a feature importance explainability method is proposed, validating that the proposed feature importance-based explainability method enhances the transparency and reliability of the DRL model for active corrective control.

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Section: The Deep-shap Methods Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach

Li,

Liu,

Hong

et al. 2024

Front. Energy Res.

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“…SHAP was utilized because of its 3 advantages. First, SHAP is a local Interpretable Machine Learning (IML) technique that can be adjusted to become constant global explanations [34]. Therefore, explanations of both single operating points and general trend identification can be obtained.…”

Section: E Shapley Additive Explanations (Shap)mentioning

confidence: 99%

HyBiLSTM: Multivariate Bitcoin Price Forecasting Using Hybrid Time-Series Models With Bidirectional LSTM

Mardjo,

Choksuchat

2024

IEEE Access

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“…An interpretable model can serve different purposes. For example, to enhance the trustworthiness of an algorithm's output, to better understand the interactions between input variables and the model's output, and to improve our understanding of the phenomenon under study [28,43].…”

Section: Interpretability In Electricity Price Forecastingmentioning

confidence: 99%

Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study

Zamudio López,

Zareipour,

Quashie

2024

Forecasting

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This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employs two tree-based machine learning classifiers and a deterministic point forecaster; a statistical regression model. Hyperparameters for the tree-based classifiers are optimized for statistical performance based on recall, precision, and F1-score. The deterministic forecaster is adapted from the literature on electricity price forecasting for the classification task. Additionally, one tree-based model prioritizes interpretability, generating decision rules that are subsequently utilized to produce price spike forecasts. For all models, we evaluate the final statistical and economic predictive performance. The interpretable model is analyzed for the trade-off between performance and interpretability. Numerical results highlight the significance of complementing statistical performance with economic assessment in electricity price spike forecasting. All experiments utilize data from Alberta’s electricity market.

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Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit

Cited by 13 publications

References 38 publications

Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach

Data-driven active corrective control in power systems: an interpretable deep reinforcement learning approach

HyBiLSTM: Multivariate Bitcoin Price Forecasting Using Hybrid Time-Series Models With Bidirectional LSTM

Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study

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