Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems

Toubeau, Jean‐François; Bottieau, Jérémie; Wang, Yi; Vallée, François

doi:10.1109/tste.2021.3092137

Cited by 31 publications

(15 citation statements)

References 34 publications

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“…To evaluate the trade-off between both concepts, we use the quantile loss. It has indeed been shown the quantile loss yields consistent outcomes with other metrics such as the Winkler score, and the continuous ranked probability score (CRPS) [47].…”

Section: B Performance Metricsmentioning

confidence: 78%

Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Toubeau

Teng

Morstyn

et al. 2023

IEEE Trans. Smart Grid

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This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of massive integration of distributed energy resources. However, traditional forecasting tasks are carried out centrally, by gathering all individual information in a single database, thus exposing private aspects of end-users. To avoid such privacy issues, this work relies on a distributed learning scheme, known as federated learning wherein individuals' data are kept decentralized. The learning procedure is then augmented with differential privacy, which offers formal resilience guarantees that the trained model cannot be reversed-engineered to infer sensitive local information. Moreover, the problem is framed using cross-series learning, which allows to smoothly integrate any new client joining the community (i.e., cold-start forecasting) without being plagued by data scarcity. Outcomes show that the proposed approach can achieve a trade-off between privacy and model performance for different architectures of deep learning networks.

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Section: B Performance Metricsmentioning

confidence: 78%

Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Toubeau

Teng

Morstyn

et al. 2023

IEEE Trans. Smart Grid

Self Cite

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“…In addition, they demonstrated a demand response-oriented application scenario. In [157], a model based on the attention mechanism and encoder-decoder structure were proposed for area control error prediction in a renewable energy-dominated power system. The variable selection module was designed to provide insights into the relative importance of features.…”

Section: Othersmentioning

confidence: 99%

Review on Interpretable Machine Learning in Smart Grid

Liao

et al. 2022

Energies

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In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

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“…Lim et al [26] proposed a temporal fusion transformer for multi-step time-series forecasting. Toubeau et al [27] proposed a model based on an attention mechanism and encoder-decoder structure for area control-error prediction. This two models use a variable selection network to choose important variables, but cannot capture the variable-wise temporal dynamics.…”

Section: Introductionmentioning

confidence: 99%

Interpretable LSTM Based on Mixture Attention Mechanism for Multi-Step Residential Load Forecasting

Zhou

2022

Electronics

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Residential load forecasting is of great significance to improve the energy efficiency of smart home services. Deep-learning techniques, i.e., long short-term memory (LSTM) neural networks, can considerably improve the performance of prediction models. However, these black-box networks are generally unexplainable, which creates an obstacle for the customer to deeply understand forecasting results and rapidly respond to uncertain circumstances, as practical engineering requires a high standard of prediction reliability. In this paper, an interpretable deep-learning method is proposed to solve the multi-step residential load forecasting problem which is referred to as explainable artificial intelligence (XAI). An encoder–decoder network architecture based on multi-variable LSTM (MV-LSTM) is developed for the multi-step probabilistic-load forecasting. The mixture attention mechanism is introduced in each prediction time step to better capture the different temporal dynamics of multivariate sequence in an interpretable form. By evaluating the contribution of each variable to the forecast, multi-quantile forecasts at multiple future time steps can be generated. The experiments on the real data set show that the proposed method can achieve good prediction performance while providing valuable explanations for the prediction results. The findings help end users gain insights into the forecasting model, bridging the gap between them and advanced deep-learning techniques.

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Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems

Cited by 31 publications

References 34 publications

Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

Review on Interpretable Machine Learning in Smart Grid

Interpretable LSTM Based on Mixture Attention Mechanism for Multi-Step Residential Load Forecasting

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