CLEB: A Continual Learning Energy Bidding Framework For An Energy Market Bidding Application

Energy trading in the day-ahead and continuous energy market enables the maximization of profits for market participants, such as utility companies/suppliers and residential/industrial consumers. However, in practice, the AI-based decision-making process for accepting or rejecting bids/offers from customers/suppliers, commonly referred to as bidding decisions, often experiences performance degradation due to the fluctuation of renewable energy resources and the intermittent demand behavior of customers. This phenomenon is widely recognized as a data distribution shift in machine learning. One conventional approach involves training the model from scratch over an extended historical period, incurring significant computational and storage costs. To address this challenge more effectively, we propose a Continual Learning-based Energy Bidding framework (CLEB). This framework employs a relay-based continual learning method, utilizing a combination of a small portion of historical data and the most recent data with different distributions to enhance the accuracy of bidding decisions. The framework consists of predictive neural networks, specifically a Multi-Layer Perceptron (MLP), as well as data buffers for storing newly acquired data from a non-stationary data stream within an application. Subsequently, the evolving probability distribution of the data stream identified by the framework is utilized to retrain the model. Our evaluation in a public European energy trading dataset shows that the framework significantly improves accuracy performance of prediction model under the data distribution shift occurrences, allowing the model adaptively itself to deal with non-stationary data distributions in dynamic environments.