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ORIGINAL RESEARCH article

Front. Energy Effic.
Sec. Energy Efficiency Applications
Volume 2 - 2024 | doi: 10.3389/fenef.2024.1502854

A Novel Simulation and Supervised Machine Learning-based Prediction Framework to Predict the Total Transportation and Energy Costs for Single-Family Households

Provisionally accepted
Vinay Gonela Vinay Gonela 1*Atif Osmani Atif Osmani 2Raghavan Srinivasan Raghavan Srinivasan 3
  • 1 Texas A&M University Central Texas, Killeen, United States
  • 2 Minnesota State University Moorhead, Moorhead, Minnesota, United States
  • 3 Ball State University, Muncie, Indiana, United States

The final, formatted version of the article will be published soon.

    This paper focuses on predicting the total transportation and energy costs (TTEC) for single-family households. A system boundary consisting of grid-powered electricity (GE) and solar-powered electricity (SE) as energy inputs and transportation vehicles that include Gasoline Vehicles (GV) and Electric Vehicles (EV) as transportation methods for energy outputs is studied. A novel threestage evaluation framework is proposed to predict the TTEC under varying single-family household parameters. In the first stage, an energy balance simulation model is proposed to estimate the TTEC for an individual household. In the second stage, the simulation model is run several times under varying parameters to develop synthetic data that is used as input for the third stage supervised machine learning (SML) models. In the third stage, numerous SML models are trained and tested to determine the best SML model that enables us to predict the TTEC with high accuracy. This best SML model can be used as a substitute for simulation model, thereby reducing the computation burden of running the simulation model for each new single-family household. A case study of single-family households in Central Texas in the US is used as an application of the framework. The results indicate that regression SML models are best in predicting the total costs with an adjusted R-squared of 99.13% and 98.88% on training and testing datasets, respectively. In addition, the parameter analysis of regression SML models suggests that the house size, number of GVs, number of EVs, EV and GV ownership costs, and solar implementation at households are the most important parameters to predict TTEC for single-family households. Counterintuitively, number of residents, GV and EV ownership, solar system size, battery capacity and peak solar hours are not significant parameters that contribute to TTEC prediction.

    Keywords: simulation, Supervised machine learning, Energy costs, transportation costs, solar-powered electricity generation, Electric Vehicles, Gasoline vehicles

    Received: 27 Sep 2024; Accepted: 11 Nov 2024.

    Copyright: © 2024 Gonela, Osmani and Srinivasan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Vinay Gonela, Texas A&M University Central Texas, Killeen, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.