Work

An Assessment of Energy Demand Using Short-Term Load Forecasting

Year: 2025

Type: conference-paper

Abstract: 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.

Source: IEEE PES/IAS PowerAfrica Conference (PowerAfrica)

DOI: 10.1109/powerafrica65840.2025.11289118

Authors: Deans David Nyathi, Pitshou Ntambu Bokoro, Mohamed Emad Farrag, Adelaide Sheik

Institutions: University of Johannesburg, Glasgow Caledonian University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: green

Funders: Glasgow Caledonian University, University of Johannesburg