Forecasting imbalance price densities with statistical methods and neural networks

Ganesh, V N and Bunn, D W (2023) Forecasting imbalance price densities with statistical methods and neural networks. IEEE Transactions on Energy Markets, Policy and Regulation. ISSN 0885-8969 (In Press)

Abstract

Despite the extensive research on electricity price forecasting, forecasting imbalance prices is a relatively new topic. Interest, however, is growing because of the greater uncertainties and costs involved in real-time balancing. Whilst there has been previous work on nonlinear statistical methods, this paper reports on a comparative study involving these and various neural network architectures including N-BEATS, fully connected, attention-based, and recurrent neural networks. To ensure valid comparability, these different neural networks were tested on the same data from Britain used in the previous point and density forecasting research. While there are only marginal improvements in point forecasts, we find that neural networks produce significantly more accurate density forecasts. Since the risks involved with exposure to imbalance prices are becoming a serious consideration for market participants, accurate density forecasts are crucial for risk management.

More Details

Item Type: Article
Subject Areas: Management Science and Operations
Date Deposited: 17 Jul 2023 13:11
Date of first compliant deposit: 30 Jun 2023
Subjects: Pricing
Price theory
Energy resources
Last Modified: 04 Aug 2023 01:58
URI: https://lbsresearch.london.edu/id/eprint/2938
More

Export and Share


Download

Accepted Version - Text
  • Restricted to Repository staff only

Statistics

Altmetrics
View details on Dimensions' website

Downloads from LBS Research Online

View details

Actions (login required)

Edit Item Edit Item