Power Play: Can Foundation Models Predict Electricity Prices?

by

Author: Denis Avetisyan

A new study benchmarks the performance of advanced time series models against traditional deep learning methods for forecasting day-ahead electricity prices in key European markets.

The study evaluates forecasting models-ranging from a simple baseline to advanced foundation models like Moirai and ChronosX-across varying data configurations, including a single bidding zone (DE-LU) and its fourteen neighbors, with feature sets encompassing electricity prices, calendar information, and fundamental load data, to systematically assess the impact of feature selection, pretraining strategies, and exposure to DE-LU data-specifically through zero-shot, one-shot, and few-shot learning scenarios-on forecasting performance and to establish a performance baseline across three model sizes (tiny/small/base or small/base/large).
The study evaluates forecasting models-ranging from a simple baseline to advanced foundation models like Moirai and ChronosX-across varying data configurations, including a single bidding zone (DE-LU) and its fourteen neighbors, with feature sets encompassing electricity prices, calendar information, and fundamental load data, to systematically assess the impact of feature selection, pretraining strategies, and exposure to DE-LU data-specifically through zero-shot, one-shot, and few-shot learning scenarios-on forecasting performance and to establish a performance baseline across three model sizes (tiny/small/base or small/base/large).

Researchers assess the trade-off between performance and computational efficiency of foundation models for probabilistic electricity price forecasting in Germany and Luxembourg.

Accurate electricity price forecasting is increasingly critical given the growing volatility introduced by large-scale renewable energy integration. This study, ‘Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting’, benchmarks time series foundation models against established deep learning techniques for day-ahead probabilistic price prediction in European markets. Results demonstrate that while foundation models offer strong performance, conventional models-particularly those enhanced with feature engineering or transfer learning-remain highly competitive, achieving comparable accuracy with potentially lower computational cost. This raises the question of whether the marginal gains offered by foundation models consistently justify their increased complexity and resource demands in practical forecasting applications.

The Inevitable Chaos of Energy Prediction

Electricity price prediction is becoming increasingly difficult as conventional time series forecasting methods falter when faced with heightened market volatility. This instability stems from two primary forces: the growing integration of renewable energy sources, like solar and wind, which are inherently intermittent and dependent on weather patterns, and the expansion of cross-border electricity exchanges. These exchanges, while promoting efficiency, introduce complex interdependencies and transmit price shocks across wider geographic areas. Consequently, historical data – the foundation of traditional forecasting – becomes less reliable as a predictor of future price movements, requiring new approaches capable of adapting to these dynamic and often unpredictable conditions. The result is a need for more sophisticated models that can account for the unique characteristics of modern energy markets and mitigate the risks associated with price fluctuations.

The efficient operation of modern electricity markets, and indeed the successful transition to a decarbonized energy system, fundamentally relies on the ability to accurately anticipate future price fluctuations. Precise electricity price forecasting empowers market participants – from power generators and suppliers to large industrial consumers – to make informed trading decisions, optimizing their strategies and reducing financial risk. Beyond commercial considerations, accurate forecasts are indispensable for system operators tasked with maintaining a stable and reliable electricity grid; they enable proactive resource allocation, effective management of intermittent renewable sources like wind and solar, and minimization of costly imbalances. Ultimately, improved forecasting capabilities translate directly into lower electricity costs for consumers and a more sustainable, economically viable pathway towards a cleaner energy future.

Traditional electricity price predictions, often delivered as single ‘point’ estimates, increasingly fail to adequately represent the real-world risks facing energy markets. A singular forecast overlooks the inherent randomness introduced by fluctuating renewable generation, unpredictable demand, and interconnected trading networks. Probabilistic forecasting addresses this limitation by generating a range of possible future outcomes, each accompanied by an associated probability. This allows market participants – from power plant operators to grid managers – to quantify potential losses and gains, make more informed decisions under uncertainty, and optimize resource allocation. Instead of simply knowing what the price might be, stakeholders gain critical insight into how likely different price scenarios are, enabling robust risk management and bolstering the resilience of a rapidly evolving energy system.

Electricity markets are experiencing a surge in complexity, moving beyond simple supply and demand interactions to encompass factors like intermittent renewable generation, dynamic pricing schemes, and increasingly interconnected cross-border exchanges. Consequently, traditional forecasting techniques, often reliant on historical data and linear models, are proving inadequate. Advanced methodologies – including machine learning algorithms capable of identifying non-linear relationships, ensemble forecasting combining multiple models, and techniques incorporating weather predictions and real-time grid data – are becoming essential. These innovative approaches not only improve forecast accuracy but also offer the adaptability required to navigate a rapidly evolving energy landscape, allowing market participants and grid operators to proactively respond to unforeseen events and optimize resource allocation in an increasingly uncertain future.

Average electricity spot market prices varied across European bidding zones throughout 2024, as detailed in Appendix Section B.1.
Average electricity spot market prices varied across European bidding zones throughout 2024, as detailed in Appendix Section B.1.

Foundation Models: A Temporary Reprieve

Foundation models represent a shift in time series forecasting by utilizing pre-training on extensive, diverse datasets. This pre-training process allows the model to learn generalizable representations of temporal dynamics, enabling effective transfer learning to new forecasting tasks. Unlike traditional methods requiring task-specific training from scratch, these models can leverage learned patterns to quickly adapt to new datasets and forecasting horizons. The ability to capture complex, non-linear relationships within time series data is enhanced through the model’s exposure to a broader range of temporal patterns during pre-training, potentially improving forecast accuracy and robustness, especially in scenarios with limited labeled data.

Zero-shot and few-shot learning techniques enable foundation models to generalize to new time series forecasting tasks without extensive retraining. Zero-shot learning allows the model to make predictions on unseen datasets without any task-specific examples, relying entirely on the pre-training data and inherent pattern recognition capabilities. Few-shot learning, conversely, requires only a limited number of labeled examples from the target task – typically ranging from one to a few dozen – to adapt the pre-trained model’s weights and biases. This contrasts sharply with traditional time series models which often require hundreds or thousands of data points for effective training, significantly reducing the data and computational resources needed for deployment in new forecasting scenarios.

Moirai and ChronosX represent implementations of the foundation model approach to time series forecasting, both built upon the Transformer architecture. Moirai is characterized by its masked sequence modeling objective and ability to handle irregularly sampled time series, while ChronosX focuses on a hierarchical forecasting framework to improve accuracy and efficiency. Both models demonstrate state-of-the-art performance on various benchmark datasets, including those for energy demand and electricity pricing, consistently outperforming traditional statistical and deep learning methods. Their performance is attributed to the Transformer’s capacity to model long-range dependencies and capture complex temporal patterns within the data, combined with pre-training strategies that allow for effective transfer learning to new forecasting tasks.

The research demonstrates that time series foundation models (TSFMs) attain performance levels comparable to established probabilistic forecasting models when applied to electricity price prediction. Specifically, TSFMs were evaluated against leading methods on benchmark datasets for electricity price forecasting, achieving competitive root mean squared error (RMSE) and continuous ranked probability score (CRPS) results. This suggests TSFMs offer a viable alternative to traditional approaches, particularly benefiting from their adaptability to changing market conditions and minimal retraining requirements when faced with new data distributions or forecasting horizons. The framework’s flexibility extends to incorporating exogenous variables and handling multivariate time series, making it suitable for complex energy systems modeling.

Calibrating the Illusion of Certainty

Foundation models, while capable of producing probabilistic forecasts, frequently require calibration to ensure the predicted probabilities accurately reflect observed frequencies. A well-calibrated probabilistic forecast will, over a sufficiently large dataset, exhibit a correspondence between the predicted probability of an event and its actual rate of occurrence; for example, events assigned a 70% probability should occur approximately 70% of the time. Miscalibration can lead to systematic over- or under-estimation of uncertainty, impacting the reliability of downstream decision-making processes that rely on these forecasts. Therefore, assessing and correcting for miscalibration is a critical step in deploying foundation models for probabilistic prediction tasks.

Quantile Regression Averaging (QRA) is a post-processing technique used to improve the calibration of probabilistic forecasts generated by foundation models. It functions by training quantile regression models on the outputs of the base model, effectively mapping the raw predictions to more reliable probability estimates at various quantiles – typically ranging from 0.01 to 0.99. By averaging across these quantile regressions, QRA adjusts the predicted probabilities to better reflect the observed frequencies of events, addressing issues such as overconfidence or underconfidence in the original forecasts. This calibration is crucial for ensuring that the uncertainty estimates provided by the model are trustworthy and accurately represent the true predictive distribution, leading to more informed decision-making.

Normalizing Flows and Neural Hierarchical Interpolation for Time Series (NHITS) are deep learning techniques employed to generate probabilistic forecasts, specifically density forecasts, which predict the probability distribution of future values rather than single point estimates. Normalizing Flows achieve this by transforming a simple probability distribution, such as a Gaussian, through a series of invertible neural network layers, allowing for the modeling of complex, multi-modal distributions. NHITS, conversely, leverages a hierarchical structure and interpolation to model temporal dependencies and produce flexible density forecasts. Both methods offer advantages over traditional statistical approaches by learning directly from data and adapting to non-linear relationships, enabling the generation of more accurate and reliable uncertainty estimates for time series data.

Rigorous evaluation of probabilistic forecasts relies on scoring rules that quantify the difference between predicted distributions and observed outcomes. The Continuous Ranked Probability Score (CRPS) measures the calibration of the entire predictive distribution, while the Energy Score specifically assesses the sharpness and calibration of predictive densities. Recent comparative analyses demonstrate that Time Series Foundation Models (TSFMs) achieve comparable performance to more complex deep learning approaches such as Normalizing Flows with NHITS (Non-linear Independent Components Estimation) combined with Quantile Regression Averaging (QRA), as indicated by similar values for both CRPS and Energy Score metrics. This suggests that, with appropriate tuning, simpler models can achieve performance levels competitive with state-of-the-art density forecasting techniques.

The Illusion of Control: A Sustainable Future?

The ability to accurately predict energy prices, not just as single values but as a range of likely outcomes, is fundamentally reshaping energy trading. Sophisticated forecasting methods, increasingly reliant on powerful foundation models and rigorous calibration techniques, allow energy companies to make informed decisions about when and how to buy and sell power, minimizing financial risk and maximizing profits. This probabilistic approach goes beyond simply anticipating average prices; it quantifies the uncertainty inherent in energy markets, enabling traders to strategically position themselves for various scenarios. Consequently, optimized trading strategies – informed by these forecasts – translate directly into reduced costs for both suppliers and consumers, fostering a more efficient and economically viable energy landscape. The precision gained through these advanced models is not merely about financial gain, but about building a more stable and resilient energy system capable of adapting to fluctuating demand and supply.

The successful incorporation of renewable energy sources, such as solar and wind, hinges on the ability to accurately predict their output, a challenge addressed through advanced forecasting techniques. Intermittent renewable generation introduces volatility into the power grid, potentially leading to instability if not effectively managed. Enhanced forecasting capabilities allow grid operators to anticipate fluctuations in renewable supply, enabling proactive adjustments to conventional power generation and energy storage systems. This predictive control minimizes the risk of imbalances between supply and demand, bolstering system reliability and preventing costly outages. Consequently, improved forecasting not only facilitates a greater reliance on sustainable energy but also strengthens the overall resilience of the power grid in the face of fluctuating resource availability and increasing energy demands.

Refining energy price forecasting involves more than just historical data; incorporating features like synthetically generated price signals can significantly enhance a model’s ability to predict complex market behaviors. Studies demonstrate that while most forecasting models benefit from these enriched feature sets, the gains are not uniform; models such as NHITS+QRA exhibit a particularly strong response. This differential impact underscores the importance of thoughtful feature engineering, suggesting that certain model architectures are better equipped to leverage nuanced data inputs, ultimately leading to more accurate predictions and improved energy trading strategies.

The pursuit of accurate energy forecasting directly bolsters the development of a sustainable and resilient energy system, increasingly vital in the face of a changing climate. Improved predictive capabilities allow for optimized integration of renewable sources, reducing reliance on fossil fuels and mitigating environmental impact. Rigorous evaluation, as demonstrated by the ‘Same-Hour (Last 28 Days)’ baseline model achieving the lowest Continuous Ranked Probability Score (CRPS) on the DE-LU test set, is paramount in establishing performance benchmarks and driving further innovation in this critical field. This focus on quantifiable metrics ensures that advancements translate into tangible improvements in grid stability, resource allocation, and ultimately, a more secure and environmentally responsible energy future.

The pursuit of ever-more-complex forecasting models, as demonstrated by the benchmarking of time series foundation models against established deep learning techniques, inevitably introduces a new class of operational burdens. This paper’s findings-comparable performance with potential gains from transfer learning-feel less like a breakthrough and more like a temporary reprieve. It’s a familiar pattern: chasing marginal gains in accuracy only to discover a corresponding increase in the effort required to maintain the system. As Marvin Minsky observed, “You can make a case for anything if you have enough assumptions.” Here, the assumption is that the benefits of these foundation models will outweigh the eventual costs of adapting and scaling them, a proposition history suggests warrants careful scrutiny. The promise of transfer learning feels particularly fragile; each new market, each slightly different data stream, will demand recalibration, and with it, a fresh layer of technical debt.

What’s Next?

The exercise of applying time series foundation models to electricity price forecasting, as demonstrated, yields performance largely commensurate with established techniques. This isn’t a failure, precisely; it’s a reminder that architecture isn’t a diagram, it’s a compromise that survived deployment. The initial promise of effortless transfer learning feels, predictably, less effortless than advertised. The gains observed are incremental, secured through the familiar labor of feature engineering – a detail that suggests some laws remain stubbornly un-disrupted.

Future work will likely center not on model novelty, but on the pragmatics of maintenance. The cost of retraining these large models, the challenge of adapting to evolving market dynamics, and the inevitable drift in performance – these are the real problems looming. Everything optimized will one day be optimized back, and the lifespan of any ‘revolutionary’ framework is measured in production cycles, not academic citations.

The pursuit of probabilistic forecasting, however, remains genuinely valuable. While current approaches offer comparable point forecasts, the ability to accurately quantify uncertainty-to map the range of plausible outcomes-will be critical for managing increasingly complex, cross-border energy systems. It’s a reminder that, in this field, one doesn’t build models-one resuscitates hope.

Original article: https://arxiv.org/pdf/2604.14739.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2026-04-19 05:47