Peak demand forecasting: A comparative analysis of state-of-the-art machine learning techniques

The increasing penetration of distributed renewable energy sources and the adoption of new power-intensive appliances, such as electric vehicles and heat pumps, poses unprecedented technical challenges to the power grid, especially on the distribution level. Furthermore, with the widespread roll-out of advanced metering infrastructure (AMI), new data-driven services can be leveraged to improve distribution networks’ performance, robustness, and flexibility. Accurate peak demand forecasting is a good example of a service that can play a vital role in smart grid operations. It can unlock demand response potential and allow more cost-efficient asset management and better planning for various stakeholders, i.e., market participants or generation units. This work presents a comparative analysis of 11 state-of-the-art machine learning (ML) approaches regarding day-ahead peak demand forecasting, along with the data analysis and feature engineering processes.