Systematic Development of Short-Term Load Forecasting Models for the Electric Power Utilities: The Case of Pakistan

Aneeque A. Mir*, Zafar A. Khan, Abdullah Altmimi, Maria Badar, Kafait Ullah, Muhammad Imran, Syed Ali Abbas Kazmi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Load forecasts are fundamental inputs for the reliable and resilient operation of a power system. Globally, researchers endeavor to improve the accuracy of their forecast models. However, lack of studies detailing standardized model development procedures remains a major issue. In this regard, this study advances the knowledge of the systematic development of short-Term load forecast (STLF) models for electric power utilities. The proposed model has been developed by using hourly load (time series) of five years of an electric power utility in Pakistan. Following the investigation of previously developed load forecast models, this study addresses the challenges of STLF by utilizing multiple linear regression, bootstrap aggregated decision trees, and artificial neural networks (ANNs) as mutually competitive forecasting techniques. The study also highlights both rudimentary and advanced elements of data extraction, synthetic weather station development, and the use of elastic nets for feature space development to upscale its reproducibility at global level. Simulations showed the superior forecasting prowess of ANNs over other techniques in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) and R2 score. Furthermore, an empirical approach has been taken to underline the effects of data recency, climatic events, power cuts, human activities, and public holidays on the model's overall performance. Further analysis of the results showed how climatic variations, causing floods and heavy rainfalls, could prove detrimental for a utility's ability to forecast its load demand in future.

Original languageEnglish
Pages (from-to)140281-140297
Number of pages17
JournalIEEE Access
Early online date4 Oct 2021
Publication statusPublished - 19 Oct 2021

Bibliographical note

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  • artificial neural networks
  • Load forecast
  • multiple linear regression


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