Neural network interpretability for forecasting of aggregated renewable generation

Lu, Yucun; Murzakhanov, Ilgiz; Chatzivasileiadis, Spyros

doi:10.1109/smartgridcomm51999.2021.9631993

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…We recommend reading [72][73][74] in order to explore methods of increasing the interpretability of a forecaster. Papers that emphasize the interpretability of solar forecasting models include [61,62,75]. Taking all of this into consideration, we recommend the following methodology when building a solar forecasting model: Start the process with fundamental baseline models and progressively advance from simpler models, such as linear regression (LR), to more intricate ones, including gradient boosting (GB) models, culminating with recurrent neural networks (RNNs), which use skip connections (ResNet).…”

Section: Neural Network (Deep Learning)mentioning

confidence: 99%

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Pandžić,

Capuder

2023

Energies

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Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to address this challenge. The paper provides a discussion about building a solar forecasting model, including evaluation measures and machine learning method selection through analysed literature. Given that machine learning is data-driven, the focus of this review has been placed on data sources referenced in the literature. Open-access data sources have been compiled and explored. The main contribution of this paper is the establishment of a benchmark for assessing the performance of solar forecasting models. This benchmark utilizes the mentioned open-source datasets, offering a standardized platform for future research. It serves the crucial purpose of streamlining investigations and facilitating direct comparisons among different forecasting methodologies in the field of solar forecasting.

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Section: Neural Network (Deep Learning)mentioning

confidence: 99%

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Pandžić,

Capuder

2023

Energies

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“…Ref. [142] proposed a binary classification neural network and a regression neural network for solar power generation prediction. In order to achieve interpretability, they adopted three feature attribution methods, Integrated Gradients, Expected Gradients, and DeepLIFT to evaluate the contribution of features.…”

Section: Energy Forecastingmentioning

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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“…43 −45 Compared to other XAI methods, IG simultaneously satisfies two axioms called sensitivity and implementation invariance and shows better performance in interpreting deep learning models. 46,47 Employing IG to interpret the predictive model is a promising way to quantitatively analyze the spatiotemporal drivers of the HABs process.…”

Section: Introductionmentioning

confidence: 99%

“…Using a hybrid deep learning model to excavate the spatial and temporal information from the input data is a promising way to improve the accuracy of HAB forecasting. However, the data-driven predictive model is hard to interpret the internal mechanism of the HABs. , In recent years, strong emphases have been placed on interpretability, transparency, and reliability of deep learning models, leading to a new area of research referred to explainable artificial intelligence (XAI). − Numerous gradient-based, surrogate-model, and perturbation-based methods have been developed to measure the importance of input features to the model output. , Integrated gradients (IG) is a recently developed gradient-based method, which quantifies the attribution of each input dimension by aggregating gradients along a linear path between the sample and the baseline. − Compared to other XAI methods, IG simultaneously satisfies two axioms called sensitivity and implementation invariance and shows better performance in interpreting deep learning models. , Employing IG to interpret the predictive model is a promising way to quantitatively analyze the spatiotemporal drivers of the HABs process.…”

Section: Introductionmentioning

confidence: 99%

Mining Spatiotemporal Information for Harmful Algal Bloom Forecasting and Mechanism Interpreting

Jia,

Xu,

Jia

et al. 2024

ACS EST Water

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A multistep spatiotemporal forecasting (MSTF) network is developed through incorporating the graph convolutional network (GCN) and the long short-term memory (LSTM) network within a sequence-to-sequence (seq2seq) framework. The MSTF method can not only extract spatial and temporal information from the input data but also make multistep-ahead and continuous predictions. An MSTF-based harmful algal bloom (HAB) forecasting model is then formulated to predict the chlorophyll-a (Chl-a) concentration of the Dianchi Lake (China). The integrated gradients (IG) method is employed to interpret the trained MSTF model and quantify the attribution of each input dimension to the Chl-a prediction. Results indicate that (i) the coefficient of determination (R 2) of the MSTF model in 24-h-ahead Chl-a prediction reaches 0.926, 28.4% higher than that of the traditional LSTM model; (ii) the ammonia nitrogen (12.3%), the total phosphorus (10.2%), the total nitrogen (9.9%), and the temperature (8.6%) are significant variables for Chl-a prediction; (iii) the spatial information from neighbor lake and river stations plays an important role in the HAB forecasting, with an average contribution of 35.0%; (iv) the proposed MSTF model is also skillful in the 72-h-ahead Chl-a prediction. Results presented highlight the importance of considering both spatial and temporal dependency of monitoring data in HAB forecasting and mechanism interpreting.

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Neural network interpretability for forecasting of aggregated renewable generation

Cited by 10 publications

References 11 publications

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources

Review on Interpretable Machine Learning in Smart Grid

Mining Spatiotemporal Information for Harmful Algal Bloom Forecasting and Mechanism Interpreting

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