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Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network

Mohammad Wazed Ali, Asif bin Mustafa, Md. Aukerul Moin Shuvo, Bernhard Sick

TL;DR

A location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models.

Abstract

Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62\%, our proposed deep MQ-TCN model exhibited a remarkable 28.93\% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88\% PICP score for the load forecasting task at the NREL site using only two weeks of data.

Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network

TL;DR

A location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models.

Abstract

Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62\%, our proposed deep MQ-TCN model exhibited a remarkable 28.93\% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88\% PICP score for the load forecasting task at the NREL site using only two weeks of data.
Paper Structure (13 sections, 7 equations, 10 figures, 4 tables)

This paper contains 13 sections, 7 equations, 10 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem definition and formulation
  4. Methodology
  5. Multivariate MQ-TCN for Multi-Step Forecasting
  6. Transfer Forecasting Knowledge with Inductive TL
  7. Experimental Setup and Evaluation
  8. Data
  9. Evaluation Metrics
  10. Multivariate Multi-Step Probabilistic Load Forecasting
  11. Location based Load Forecasting under Data Scarcity
  12. Uncertainty Handling and Impact of Variable Data Size, lookback Period and Forecast Horizon in TL
  13. Conclusion and Future Work

Figures (10)

  • Figure 1: Load Forecasting at JPL and NREL Electric Vehicle Charging Sites using Deep MQ-TCN model with Inductive Transfer Learning.
  • Figure 2: Components of a TCN (a) A dilated causal convolution applied convolutional filters with dilation factors $d_l$ = 1, 2, and 4, and a filter size of k = 3; (b) In a TCN residual block, if the input and output have different sizes, a 1 × 1 convolution is included to adjust for the difference; (c) Illustration of Residual Connection within a TCN b24.
  • Figure 3: Multi-Quantile Forecasting using TCN.
  • Figure 4: Energy Consumption Trends and Comparisons for All Locations
  • Figure 5: Experimental Design for MQ-TCN Forecasting in E-Mobility Applications.
  • ...and 5 more figures