Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles

• URL: http://arxiv.org/abs/2311.16003v1
• Date: Mon, 27 Nov 2023 16:52:25 GMT
• Title: Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles
• Authors: Yuantao Fan, Zhenkan Wang, Sepideh Pashami, Slawomir Nowaczyk, Henrik Ydreskog
• Abstract summary: Energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles. In this paper, we demonstrate a potential solution by training multiple regression models on subsets of data. Experiments on both synthetic and real-world datasets show that such splitting of a complex problem into simpler ones yields better regression performance and interpretability.
• Score: 6.375656754994484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understanding why certain predictions are cast is paramount for such a predictive model to gain user trust and be deployed in practice. Since commercial vehicles operate differently as transportation tasks, ambient, and drivers vary, a heterogeneous population is expected when building an AI system for forecasting energy consumption. The dependencies between the input features and the target values are expected to also differ across sub-populations. One well-known example of such a statistical phenomenon is the Simpson paradox. In this paper, we illustrate that such a setting poses a challenge for existing XAI methods that produce global feature statistics, e.g. LIME or SHAP, causing them to yield misleading results. We demonstrate a potential solution by training multiple regression models on subsets of data. It not only leads to superior regression performance but also more relevant and consistent LIME explanations. Given that the employed groupings correspond to relevant sub-populations, the associations between the input features and the target values are consistent within each cluster but different across clusters. Experiments on both synthetic and real-world datasets show that such splitting of a complex problem into simpler ones yields better regression performance and interpretability.

Related papers

• Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand [3.7690784039257292]
  This paper studies the forecasting problem of multiple electric vehicle charging stations (EVCSs) in a hierarchical probabilistic manner. For each charging station, a deep learning model based on a partial input convex neural network (PICNN) is trained to predict the day-ahead charging demand's conditional distribution. Differentiable convex optimization layers (DCLs) are used to reconcile the scenarios sampled from the distributions to yield coherent scenarios.
  arXiv  Detail & Related papers  (2024-11-01T03:35:04Z)
• Sparse Prototype Network for Explainable Pedestrian Behavior Prediction [60.80524827122901]
  We present Sparse Prototype Network (SPN), an explainable method designed to simultaneously predict a pedestrian's future action, trajectory, and pose. Regularized by mono-semanticity and clustering constraints, the prototypes learn consistent and human-understandable features.
  arXiv  Detail & Related papers  (2024-10-16T03:33:40Z)
• GenFollower: Enhancing Car-Following Prediction with Large Language Models [11.847589952558566]
  We propose GenFollower, a novel zero-shot prompting approach that leverages large language models (LLMs) to address these challenges. We reframe car-following behavior as a language modeling problem and integrate heterogeneous inputs into structured prompts for LLMs. Experiments on Open datasets demonstrate GenFollower's superior performance and ability to provide interpretable insights.
  arXiv  Detail & Related papers  (2024-07-08T04:54:42Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Scaling Laws Do Not Scale [54.72120385955072]
  Recent work has argued that as the size of a dataset increases, the performance of a model trained on that dataset will increase. We argue that this scaling law relationship depends on metrics used to measure performance that may not correspond with how different groups of people perceive the quality of models' output. Different communities may also have values in tension with each other, leading to difficult, potentially irreconcilable choices about metrics used for model evaluations.
  arXiv  Detail & Related papers  (2023-07-05T15:32:21Z)
• Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines [0.0]
  symbolic regression is an interpretable alternative to the "black box" models. In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight. Results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3degC compared to the ground truth.
  arXiv  Detail & Related papers  (2022-03-24T15:05:32Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Injecting Knowledge in Data-driven Vehicle Trajectory Predictors [82.91398970736391]
  Vehicle trajectory prediction tasks have been commonly tackled from two perspectives: knowledge-driven or data-driven. In this paper, we propose to learn a "Realistic Residual Block" (RRB) which effectively connects these two perspectives. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty.
  arXiv  Detail & Related papers  (2021-03-08T16:03:09Z)
• A Data-Driven Machine Learning Approach for Consumer Modeling with Load Disaggregation [1.6058099298620423]
  We propose a generic class of data-driven semiparametric models derived from consumption data of residential consumers. In the first stage, disaggregation of the load into fixed and shiftable components is accomplished by means of a hybrid algorithm. In the second stage, the model parameters are estimated using an L2-norm, epsilon-insensitive regression approach.
  arXiv  Detail & Related papers  (2020-11-04T13:36:11Z)
• A Locally Adaptive Interpretable Regression [7.4267694612331905]
  Linear regression is one of the most interpretable prediction models. In this work, we introduce a locally adaptive interpretable regression (LoAIR) Our model achieves comparable or better predictive performance than the other state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-05-07T09:26:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.