Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data

• URL: http://arxiv.org/abs/2403.13246v1
• Date: Wed, 20 Mar 2024 02:17:16 GMT
• Title: Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data
• Authors: Fucai Ke, Hao Wang,
• Abstract summary: We develop a home charging prediction method using historical smart meter data. We achieve consistently high accuracy of over 96.81% across different prediction time spans.
• Score: 4.820576346277399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. However, existing prediction methods may not be suitable due to the unavailability of or limited access to home charging data. To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data. Different from NILM detecting EV charging that has already occurred, our method provides predictive information of future EV charging occurrences, thus enhancing its utility for charging management. Specifically, our method, leverages a self-attention mechanism-based transformer model, employing a ``divide-conquer'' strategy, to process historical meter data to effectively and learn EV charging representation for charging occurrence prediction. Our method enables prediction at one-minute interval hour-ahead. Experimental results demonstrate the effectiveness of our method, achieving consistently high accuracy of over 96.81\% across different prediction time spans. Notably, our method achieves high prediction performance solely using smart meter data, making it a practical and suitable solution for grid operators.

Related papers

• Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms [49.37592437398933]
  "Smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. We investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. We propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging.
  arXiv  Detail & Related papers  (2023-10-18T18:38:59Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction [7.441576351434805]
  This paper proposes a novel approach that enables the integration of graph and temporal attention mechanisms for feature extraction. Evaluation results on a dataset of 18,013 EV charging piles in Shenzhen, China show that the proposed approach, named PAG, can achieve state-of-the-art forecasting performance.
  arXiv  Detail & Related papers  (2023-09-11T06:31:45Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging [0.0]
  The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. This paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals.
  arXiv  Detail & Related papers  (2023-06-05T16:25:33Z)
• A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
  Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
  arXiv  Detail & Related papers  (2022-05-20T22:13:25Z)
• Analyzing the Travel and Charging Behavior of Electric Vehicles -- A Data-driven Approach [1.7403133838762446]
  Electric vehicles (EVs) may pose significant electricity demand on power systems. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips. We develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance.
  arXiv  Detail & Related papers  (2021-06-11T15:53:59Z)
• Short-term forecast of EV charging stations occupancy probability using big data streaming analysis [0.0]
  This paper presents an architecture able to deal with data streams from a charging infrastructure. The final aim is to forecast electric charging station availability after a set amount of minutes from present time. The streaming model performs better than a model trained only using historical data.
  arXiv  Detail & Related papers  (2021-04-26T12:03:02Z)
• PiP: Planning-informed Trajectory Prediction for Autonomous Driving [69.41885900996589]
  We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets.
  arXiv  Detail & Related papers  (2020-03-25T16:09:54Z)

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.