SmartEnergyForecast-SEF-

Table of Contents

• General Info

• Technologies used

• Project staus

• Contributors

General Info

PROBLEM STATEMENT

The energy sector in South Africa is facing challenges in managing power security and planning for capacity generation due to a lack of accurate forecasting of electricity demand, which is influenced by various factors such as economic growth, population growth, climate conditions, and changes in consumer behavior. This has resulted in power shortages and blackouts, highlighting the urgent need for accurate electricity demand forecasting to ensure reliable and sustainable electricity supply for the country's growing population and economy.

THE SOLUTION

We will use machine learning to develop short-term and long-term forecasting models that can accurately predict electricity demand and electricity loss by analyzing historical data on electricity consumption patterns, weather patterns, and demographic trends. These models can help utility companies and policymakers to effectively manage power security, plan for capacity generation, and identify areas where energy efficiency improvements can be made to reduce demand. Short-term forecasting models can also help to manage the supply and demand of electricity in real-time and avoid blackouts or brownouts. Machine learning will be able to help us ensure a reliable and secure supply of electricity for the future.

Technologies used

Python

SAS

Tableau

Project staus

Complete

Contributors

Anna Mofokeng

Edwin Ngoveni

Nompumelelo Phiri

Nontando Masimula

Wayne Sithole