A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

Jorge E. Pesantez, Binbin Li, Christopher Lee, Zhizhen Zhao, Mark Butala, Ashlynn S. Stillwell

Research output: Contribution to journalArticlepeer-review


The increasing population migration to urban and peri-urban areas increases basic service needs for cities worldwide. Residential electricity demand increases with more customers and varies with novel uses, such as charging electric vehicles, which may add additional dynamics to the residential electricity demand profile. Widespread installation of smart electricity meters provides fine temporal resolution data reflecting current demands and supports predicting future demands. As part of a demand-side management program, understanding the main drivers of current electricity usage based on demand-driven and exogenous predictors represents a valuable tool for utilities facing new demand scenarios. This work presents the application of multiple models to forecast electricity demand based on the input data and the forecasting horizon. Models with exogenous variables as predictors are part of the input–output category, including a Feed Forward Neural Network, Random Forest, and a Linear Gaussian State Space model. The second category is demand-driven models, where predictors include only previous demand values. The demand-driven models in our analysis include a univariate Nonlinear Autoregressive Neural Network and a Linear Gaussian State Space. Using smart electricity meter data from the greater Chicago area, we compare the performance of the models on two different types of accounts: single- and multi-family residential users when forecasting one and multiple steps. Results show that the Linear Gaussian model reports an R2 of 0.99 compared to an average R2 of 0.92 from the Feed Forward Neural Networks and Random Forest when forecasting single- and multi-family electricity demand one step ahead. However, Nonlinear Autoregressive Neural Networks report an average R2 of 0.85 compared to the Linear Gaussian R2 of 0.58 when forecasting 48 steps. We also found that the most important predictors for single-family demand are temporal variables like weekdays, working and non-working days, and day hours. For multi-family demand, electricity demand at the same hour as the previous week replaces weekdays as a significant predictor. Different forecasting models can assist utilities and city planners to manage demand under different and novel residential electricity usage conditions.

Original languageEnglish (US)
Article number129142
StatePublished - Nov 15 2023


  • Demand management
  • Feature importance
  • Forecasting
  • Machine learning
  • Regression

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Modeling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • Mechanical Engineering
  • General Energy
  • Management, Monitoring, Policy and Law
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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