Short-Term Electricity Load Forecasting for the Panama Power System Using an ARIMA Baseline

Abstract

The forecasting of short-term load (STLF) is an important part of the functioning of a power system, as it aids in discovering the dispatch plans and taking the strain off. This paper researches hourly short-term electricity load forecasting of the Panama power system based on a univariate Autoregressive Integrated Moving Average (ARIMA) model as a clean statistical reference. The national demand in 20152020 was chosen as the dataset obtained in the publicly available Kaggle repository. It was tested on a 24-hour-ahead forecasting problem with a train-test split that was based on chronology. The ARIMA (2,1,2) model gained the reliability of the held-out test window of 48.88 MWh, RMSE of 60.50 MWh, and MAPE of 4.32%. Although this error rate is widely similar to classical statistical thresholds in short-horizon load forecasting papers, it is nevertheless significantly larger than the accuracy commonly found by the more advanced machine learning and deep learning models, specifically when using exogenous variables. The results hence result in ARIMA being an efficient base of clarity and benchmarking, with the development being more expressive models to route the non-linearities in electricity demand.