An Assessment of Energy Demand Using Short-Term Load Forecasting

Renewable energy is crucial for mitigating the greenhouse effect, yet its integration into power systems presents challenges such as energy insufficiency during demand peaks, improper sizing of solar panels and battery storage, and unplanned downtime that can cause energy and economic losses. Accurate short-term load forecasting (STLF) is essential for improving energy demand assessment and optimizing renewable systems. This study evaluates three STLF methods, namely Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), using data from a social enterprise in South Africa. The dataset of 646 hourly observations was divided into training and testing sets, and model performance was assessed with Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The ANN model achieved the lowest forecasting error, making it the most suitable approach. Accurate STLF improves system sizing and operation, reducing inefficiencies and supporting clean energy deployment.