Peak demand forecasting: A comparative analysis of state-of-the-art machine learning techniques
2022 2nd International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)(2022)
Abstract
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.
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Key words
Artificial neural networks,convolutional neural network,linear regression,long short-term memory,machine learning,peak demand forecasting,tree-based models
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