To reduce the heavy computational burden of reactive power optimization of
distribution networks, machine learning models are receiving increasing
attention. However, most machine learning models (e.g., neural networks) are
usually considered as black boxes, making it challenging for power system
operators to identify and comprehend potential biases or errors in the
decision-making process of machine learning models. To address this issue, an
explainable machine-learning framework is proposed to optimize the reactive
power in distribution networks. Firstly, a Shapley additive explanation
framework is presented to measure the contribution of each input feature to the
solution of reactive power optimizations generated from machine learning
models. Secondly, a model-agnostic approximation method is developed to
estimate Shapley values, so as to avoid the heavy computational burden
associated with direct calculations of Shapley values. The simulation results
show that the proposed explainable framework can accurately explain the
solution of the machine learning model-based reactive power optimization by
using visual analytics, from both global and instance perspectives. Moreover,
the proposed explainable framework is model-agnostic, and thus applicable to
various models (e.g., neural networks).

提出了一个可解释的机器学习框架来优化配电网络中的无功功率，通过全局和实例视角的可视分析，能够准确解释基于机器学习模型的无功功率优化解决方案。

基于机器学习的配电网无功功率优化的可解释框架

An Explainable Framework for Machine learning-Based Reactive Power  Optimization of Distribution Network

A very popular model-agnostic technique for explaining predictive models is
the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP
are a conditional expectation version and an unconditional expectation version
(the latter is also known as interventional SHAP). Except for tree-based
methods, usually the unconditional version is used (for computational reasons).
We provide a (surrogate) neural network approach which allows us to efficiently
calculate the conditional version for both neural networks and other regression
models, and which properly considers the dependence structure in the feature
components. This proposal is also useful to provide drop1 and anova analyses in
complex regression models which are similar to their generalized linear model
(GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart
that considers the right dependence structure in the feature components.

为了解释预测模型，我们提出了一种基于模型的 SHapley Additive exPlanation (SHAP) 的技术，利用神经网络方法高效计算神经网络和回归模型的条件版本，并恰当考虑特征分量的依赖结构，此方法在复杂回归模型中提供了 drop1 和 anova 分析，并通过 partial dependence plot (PDP) 对特征分量的正确依赖结构进行建模。

SHAP 的条件期望网络

Conditional expectation network for SHAP

With modern infotainment systems, drivers are increasingly tempted to engage
in secondary tasks while driving. Since distracted driving is already one of
the main causes of fatal accidents, in-vehicle touchscreen Human-Machine
Interfaces (HMIs) must be as little distracting as possible. To ensure that
these systems are safe to use, they undergo elaborate and expensive empirical
testing, requiring fully functional prototypes. Thus, early-stage methods
informing designers about the implication their design may have on driver
distraction are of great value. This paper presents a machine learning method
that, based on anticipated usage scenarios, predicts the visual demand of
in-vehicle touchscreen interactions and provides local and global explanations
of the factors influencing drivers' visual attention allocation. The approach
is based on large-scale natural driving data continuously collected from
production line vehicles and employs the SHapley Additive exPlanation (SHAP)
method to provide explanations leveraging informed design decisions. Our
approach is more accurate than related work and identifies interactions during
which long glances occur with 68 % accuracy and predicts the total glance
duration with a mean error of 2.4 s. Our explanations replicate the results of
various recent studies and provide fast and easily accessible insights into the
effect of UI elements, driving automation, and vehicle speed on driver
distraction. The system can not only help designers to evaluate current designs
but also help them to better anticipate and understand the implications their
design decisions might have on future designs.

本文利用大量自然驾驶数据和机器学习方法，预测车内触摸屏界面的可视需求和驾驶员视觉关注分配的影响因素，并提供局部和全局的解释说明，以帮助设计师评估当前设计并更好地预测和理解其对未来设计所可能产生的影响。