Parsimonious Short-Term Load Forecasting for Optimal Operation Planning of Electrical Distribution Systems

Juan Camilo López*, Marcos J. Rider, Qiuwei Wu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

183 Downloads (Pure)


The optimal operation planning (OOP) of electrical distribution systems (EDS) is very sensible to the quality of the short-term load forecasts. Assuming aggregated demands in EDS as univariate non-stationary seasonal time series, and based on historical measurements gathered by smart meters, this paper presents a parsimonious short-term load forecasting method to estimate the expected outcomes of future demands, and the standard deviations of forecast errors. The chosen short-term load forecasting method is an adaptation of the multiplicative autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models are parsimonious forecasting techniques because they require very few parameters and low computational resources to provide an adequate representation of stochastic time series. Two approaches are used in this paper to estimate the parameters that constitute the proposed multiplicative ARIMA model: a frequentist and a Bayesian approach. Advantages and disadvantages of both methods are compared by simulating a centralized self-healing scheme of a real EDS that uses the forecasts to deploy a robust restoration plan. Results shown that the proposed seasonal ARIMA model is a fast, precise, straightforward and adaptable load forecasting method, suitable for OOP of highly supervised EDS.
Original languageEnglish
JournalI E E E Transactions on Power Systems
Issue number2
Pages (from-to)1427 - 1437
Publication statusPublished - 2018


  • Electrical distribution systems
  • Frequentist and Bayesian approach
  • Optimal operation planning
  • Seasonal ARIMA models
  • Short-term load forecasting


Dive into the research topics of 'Parsimonious Short-Term Load Forecasting for Optimal Operation Planning of Electrical Distribution Systems'. Together they form a unique fingerprint.

Cite this