A PyTorch-Based Long Short-Term Memory (LSTM) Model for Short-Term Electricity Load Forecasting in South Australia

Abstract

Accurate electricity load forecasting is important for power system operation, energy planning and demand management. With the increasing use of data-driven methods, deep learning models have become useful tools for analysing complex electricity demand patterns. This study develops a PyTorch-based LSTM model for short-term electricity load forecasting, using South Australian electricity demand data from 2024. The original dataset consists of chronological five-minute demand records, and total electricity demand is selected as the target variable. Missing values were checked, the demand series was normalised using training-set statistics, and supervised learning samples were generated by a sliding window method. In the proposed model, the previous 24 five-minute demand observations are used to predict the next demand value. The dataset is divided into training, validation and testing subsets in chronological order. The model is trained using mean squared error loss and the Adam optimiser with a step learning-rate schedule. The results show that the LSTM model can follow the main movement of electricity demand, but the prediction curve is smoother than the actual curve and some short-term fluctuations remain difficult to capture. This limitation is mainly caused by the use of a simple univariate input, because weather, calendar, electricity price and renewable generation variables are not included. Future work should introduce multivariate features, compare LSTM against benchmark models such as GRU and ARIMA, and test longer input windows to further improve forecasting accuracy.