Industry
Energy
Client
Energy tech startup
Demand Forecasting with Machine Learning: A Case Study
About project
Accurate Electricity Demand Forecasting in the Energy Sector
In the rapidly evolving energy sector, the ability to accurately forecast electricity demand has never been more critical. With the increasing prevalence of smart meters and visibility into low-voltage networks, we now have the potential to predict future electricity demand from the most granular level—individual assets—to the highest level of aggregation, such as national demand.
This capability is essential for optimizing energy distribution, ensuring grid stability, and facilitating effective demand response strategies. Demand response, a method to adjust electricity consumption in response to supply changes, requires precise forecasting from real-time to day-ahead to maximize its benefits.
Problem
Accurately forecasting electricity demand has become crucial for optimizing energy distribution, maintaining grid stability, and enhancing demand response strategies.
Despite its importance, demand forecasting presents several challenges, primarily due to the complex interplay of various factors that influence electricity demand.
Weather Sensitivity: Predictions can be significantly affected by unpredictable weather changes, making accurate forecasting difficult.
Variable Consumer Behavior: Unpredictable consumer behaviors, influenced by holidays and special events, complicate demand projections.
Renewable Energy Integration: The variability of renewable energy sources like wind and solar adds unpredictability to the energy supply, complicating demand forecasting.
