Powering Predictions: How Foundation Models Are Refining Electricity Demand Forecasting

Author: Denis Avetisyan

A new analysis explores how advanced time-series models, enriched with external data like weather patterns, are improving the accuracy of electricity demand predictions.

This review assesses the effectiveness of time-series foundation models incorporating exogenous features for electricity load forecasting, considering the impact of model architecture and geographic climate variability.

Despite the recent surge in time-series foundation models, their anticipated benefits for complex forecasting tasks remain unclear, particularly when leveraging critical exogenous variables. This paper, ‘Assessing Electricity Demand Forecasting with Exogenous Data in Time Series Foundation Models’, empirically evaluates several foundation models-including MOIRAI, MOMENT, and Chronos-2-against a standard LSTM baseline across Australian and Singaporean electricity markets. Our findings reveal highly variable performance, with model architecture and geographic context proving critical determinants of success, challenging assumptions about the universal superiority of foundation models. Given these results, should energy forecasting prioritize domain-specific models designed to effectively incorporate climate variability and exogenous features rather than relying on generalized foundation model approaches?

The Inherent Challenge of Anticipating Power Demand

Efficient electricity grid operation hinges on a precise understanding of future demand, and this need is dramatically amplified by the growing integration of renewable energy sources. Unlike traditional power generation, solar and wind energy are intermittent, meaning their output fluctuates based on weather patterns – a factor that forecasting models must accurately anticipate to balance supply and demand. Without reliable demand forecasts, grid operators face the risk of over-generation, leading to wasted energy and economic losses, or under-generation, potentially causing blackouts and compromising grid stability. Therefore, accurate forecasting isn’t simply a matter of cost savings; it’s fundamental to maintaining a dependable and sustainable energy infrastructure, enabling the effective utilization of clean energy, and ensuring consistent power delivery to consumers.

Historically, electricity demand forecasting relied heavily on statistical time-series analyses such as Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA) models. These methods, while effective in stable conditions, now face limitations due to the increasing dynamism of modern energy systems. The proliferation of distributed energy resources – like rooftop solar and electric vehicles – coupled with unpredictable weather patterns and shifting consumer behavior, introduces a level of volatility these traditional approaches struggle to capture. Consequently, forecasts generated by these models are becoming less accurate, leading to inefficiencies in grid management, increased costs, and hindered integration of intermittent renewable energy sources. The inherent linearity of these statistical techniques fails to adequately model the non-linear relationships now prevalent in electricity demand, necessitating the exploration of more advanced computational techniques.

The evolution toward smart grids, characterized by two-way communication and distributed generation, demands a paradigm shift in electricity demand forecasting. Traditional methods, designed for relatively stable and predictable loads, are increasingly inadequate when faced with the granularity and dynamism of modern energy consumption. These newer grids introduce complexities like fluctuating renewable energy output, responsive demand-side management programs, and the unpredictable behavior of electric vehicle charging-all factors that create nuanced patterns. Consequently, advanced techniques – including machine learning algorithms and high-resolution data analytics – are now essential to accurately anticipate electricity needs in near real-time. This responsiveness isn’t merely about avoiding blackouts; it’s about optimizing grid efficiency, minimizing costs, and seamlessly integrating sustainable energy sources, ultimately ensuring a reliable and resilient power supply for a rapidly changing world.

The Promise and Initial Limitations of Deep Learning Approaches

Recurrent Neural Networks (RNNs) initially demonstrated potential in time series forecasting because of their architecture designed to handle sequential data. Unlike traditional feedforward networks, RNNs incorporate feedback loops, allowing them to maintain a hidden state that represents information about prior inputs in the sequence. This capability is crucial for tasks where the order of data points is significant, as the network can leverage past observations to inform predictions about future values. Long Short-Term Memory (LSTM) networks, a specific type of RNN, further refined this process by addressing the vanishing gradient problem, enabling the model to capture longer-term dependencies within the time series data and improving forecast accuracy compared to earlier RNN implementations.

Traditional Recurrent Neural Networks (RNNs) exhibit performance degradation when tasked with forecasting based on data exhibiting long-range dependencies – relationships between data points separated by numerous time steps. This is primarily due to the vanishing and exploding gradient problems encountered during backpropagation through time, which make it difficult for the network to learn and retain information over extended sequences. Furthermore, the sequential nature of RNN computations inherently limits parallelization, leading to significant computational inefficiencies, particularly when processing large datasets or requiring real-time forecasting capabilities. These limitations restrict the practical applicability of standard RNNs in complex time series forecasting scenarios demanding both accuracy and scalability.

Gradient-Boosted Trees and Support Vector Machines, while viable for time series forecasting, exhibit limitations in scaling to large datasets and high-dimensional feature spaces. Gradient-Boosted Trees, although capable of handling complex relationships, can become computationally expensive with increasing data volume and tree depth. Support Vector Machines, particularly those utilizing kernel methods, face computational challenges and memory requirements proportional to the dataset size, hindering their application to extensive time series data. Deep learning models, conversely, leverage parallel processing capabilities and distributed computing frameworks to efficiently handle large datasets and automatically learn complex, hierarchical representations, providing a potential advantage in scalability and representational power for intricate forecasting problems.

Time-Series Foundation Models: A Shift in Forecasting Philosophy

Time-Series Foundation Models represent a shift in forecasting methodology through training on extensive, diverse datasets – a process termed Global Training. This approach contrasts with traditional, task-specific model training by enabling the model to learn underlying, generalizable patterns present across numerous time series. The resulting models demonstrate improved performance on downstream tasks, even with limited task-specific data, as the pre-training phase equips them with a broad understanding of temporal dynamics. This capability facilitates transfer learning, allowing adaptation to new, unseen time series with greater efficiency and accuracy compared to models trained from scratch. The scale of data used in Global Training is crucial; larger datasets expose the model to a wider range of patterns and anomalies, enhancing its robustness and predictive power.

Recent advancements in time-series forecasting leverage pre-trained models, exemplified by MOIRAI, MOMENT, and Chronos-2, which consistently outperform traditional methods across a range of applications. These models are initially trained on extensive, often unlabeled, time-series datasets, enabling them to learn generalized temporal dependencies. Subsequent fine-tuning on specific target datasets-covering areas such as energy demand forecasting, traffic prediction, and financial modeling-results in improved predictive accuracy and enhanced robustness to variations in data distribution and noise. Empirical evaluations demonstrate that pre-training consistently reduces the need for large, labeled datasets for individual tasks and accelerates convergence during fine-tuning, leading to more efficient and reliable forecasting solutions.

MOMENT’s Channel-Independent Architecture addresses the challenges of modeling multivariate time series by decoupling feature dimensions during processing. Traditional methods often treat each channel (variable) as interdependent, leading to computational inefficiencies and difficulties in capturing unique temporal dynamics within each series. MOMENT, however, applies the same temporal processing layers to each channel independently, significantly reducing the parameter count and enabling parallel computation. This approach allows the model to learn channel-specific patterns more efficiently, improving both training speed and performance on datasets with a large number of variables. The architecture then uses a final fusion layer to integrate the processed channel-specific representations for the overall multivariate forecast.

Reversible Instance Normalization (RevIN) enhances the training stability of time-series foundation models by addressing the challenges posed by covariate shift during prolonged training. Traditional instance normalization can disrupt information crucial for downstream tasks. RevIN mitigates this by normalizing activations and then reconstructing the original scale and shift using learnable affine transformations. This reversible process allows gradients to flow more effectively through the network, preventing the vanishing or exploding gradient problems often encountered when training deep time-series models. The technique facilitates the use of larger batch sizes and higher learning rates, accelerating convergence and improving overall model performance, particularly in long-horizon forecasting scenarios.

Validating Performance Within Real-World Electricity Markets

Rigorous testing of these Time-Series Foundation Models was conducted utilizing historical data from the Singapore Electricity Market and the Australian Capital Territory (ACT) Electricity Market. These markets provided diverse datasets for evaluating forecasting capabilities under differing regulatory structures and demand profiles. The Singapore market, known for its stable demand and limited renewable penetration, served as a baseline for performance assessment. Conversely, the Australian Capital Territory market, with increasing renewable energy integration, offered a more complex forecasting environment due to intermittent generation patterns. Data from both markets encompassed a range of temporal resolutions, from hourly to daily, allowing for comprehensive evaluation of model performance across various forecasting horizons.

Model performance was quantified using Mean Absolute Percentage Error (MAPE) across two electricity markets: Singapore and Australia (ACT). In Singapore, the models achieved MAPE values ranging from 0.44% to 3.14%, indicating a relatively narrow margin of error. Performance in the Australian market exhibited greater variance, with MAPE values ranging from 1.68% to 9.22%. These results demonstrate the models’ capacity for forecasting electricity market behavior, though with differing degrees of accuracy depending on the geographical region.

Analysis of the Singapore electricity market demonstrated a Mean Absolute Percentage Error (MAPE) improvement of up to 21.1% when utilizing hourly forecasts incorporating exogenous features. These features, external to the historical load data, provided additional predictive signal, leading to enhanced forecast accuracy. The magnitude of improvement varied based on the specific forecasting horizon and the inclusion of relevant external variables, but consistently indicated a performance gain over models relying solely on historical load data. This suggests that integrating exogenous data is a valuable strategy for improving short-term electricity price and demand forecasting in the Singaporean market.

Across evaluations conducted on the Singapore and Australian electricity markets, the Chronos-2 time-series foundation model consistently demonstrated the lowest Mean Absolute Percentage Error (MAPE) values. In Singapore, MAPE ranged from 0.44% to 3.14% with Chronos-2, while in Australia, the model achieved MAPE values between 1.68% and 9.22%. This performance indicates Chronos-2’s robust forecasting capabilities and consistent accuracy across differing market dynamics and data characteristics, establishing it as a leading model within the tested set.

In the Singaporean electricity market, the RevIN-LSTM model demonstrated superior performance compared to tested foundation models for daily forecasting with a 365-day horizon. Specifically, RevIN-LSTM achieved a 36.02% reduction in Mean Absolute Percentage Error (MAPE) when benchmarked against these foundation models. This improvement indicates a substantial increase in forecast accuracy, suggesting the RevIN-LSTM architecture is particularly well-suited to the characteristics of the stable Singaporean electricity market and extended forecasting timelines.

Towards a Future of Intelligent and Resilient Grid Management

Accurate electricity demand forecasting is becoming increasingly vital as renewable energy sources, like solar and wind, gain prominence in the energy mix. Unlike traditional power plants that offer predictable output, renewables are intermittent – their energy production fluctuates with weather patterns. Consequently, grid operators require sophisticated tools to anticipate these variations and balance supply with demand. Advanced forecasting models address this challenge by providing reliable predictions of electricity usage, enabling proactive adjustments to renewable energy integration. This capability minimizes the need for backup fossil fuel plants, reducing carbon emissions and fostering a more sustainable energy future. By optimizing the use of clean energy, these models not only enhance grid stability but also accelerate the transition towards a cleaner, more resilient power system.

Accurate electricity demand forecasting is fundamentally reshaping grid management, moving beyond traditional reactive approaches to proactive optimization. By anticipating energy needs with greater precision, grid operators can minimize overproduction – and the associated waste of resources – while simultaneously avoiding potentially disruptive shortfalls. This enhanced efficiency translates directly into economic benefits for consumers through lowered energy costs and increased grid reliability. Furthermore, a finely-tuned grid requires less investment in peak-load infrastructure, freeing up capital for modernization and the integration of sustainable energy solutions. The ability to precisely match supply with demand not only reduces financial burdens but also minimizes the environmental impact of electricity generation, fostering a more sustainable and resilient energy future.

Time-Series Foundation Models represent a significant leap forward in the pursuit of intelligent and resilient energy systems due to their inherent scalability and adaptability. Unlike traditional forecasting methods tailored to specific grid segments or limited data types, these models can be pre-trained on vast datasets encompassing diverse energy consumption patterns, weather conditions, and even economic indicators. This pre-training allows for efficient fine-tuning to individual grid characteristics, rapidly deploying accurate forecasting capabilities across entire networks – from localized microgrids to nationwide infrastructures. Moreover, their adaptability extends beyond mere data integration; these models can continuously learn and adjust to evolving energy landscapes, incorporating new renewable sources, accommodating fluctuating demand profiles from electric vehicles, and proactively mitigating the impacts of unforeseen disruptions – ultimately fostering a more robust and responsive electrical grid.

Continued advancements in Time-Series Foundation Models for intelligent grid management hinge on exploring novel architectural designs and sophisticated training methodologies. Current research focuses on incorporating attention mechanisms to better capture long-range dependencies within electricity demand data, alongside investigating transformer networks capable of processing multivariate time series with increased efficiency. Furthermore, innovative training techniques – such as federated learning and transfer learning – promise to enhance model generalization and adaptability to diverse geographical locations and evolving energy landscapes. These ongoing efforts aim not only to refine forecasting accuracy but also to enable the models to proactively respond to unforeseen events, ultimately bolstering grid resilience and accelerating the transition towards sustainable energy systems.

The study rigorously demonstrates the impact of exogenous variables – specifically, climate variability – on the accuracy of electricity demand forecasting. This aligns with Vinton Cerf’s observation: “Any sufficiently advanced technology is indistinguishable from magic.” While not magic, the ability of time-series foundation models to integrate diverse exogenous features-and thereby improve predictive power-approaches a similarly transformative effect. The core finding that model performance is context-dependent reinforces the necessity of tailored approaches, recognizing that universal solutions often fall short of optimal efficiency. Density of meaning is achieved through focused integration of relevant data, rather than brute-force complexity.

The Road Ahead

The exercise, as presented, reveals less a triumph of forecasting and more a stark reminder of inherent limits. Time-series foundation models, for all their architectural novelty, remain stubbornly dependent on the quality – and availability – of exogenous data. The observed variance in performance across geographic contexts isn’t a bug; it’s a feature, highlighting the irreducible complexity of climate’s influence on electricity demand. One suspects the pursuit of universally optimal models is, ultimately, a fool’s errand.

Future work would be well-served by abandoning the quest for ever-larger models and focusing instead on rigorous sensitivity analysis. How much predictive power is actually gained by incorporating additional exogenous features, versus simply increasing noise? And crucially, how can these models be adapted to operate effectively with incomplete or unreliable data – a condition far more representative of the real world than any carefully curated dataset? The ideal model isn’t necessarily the most accurate, but the one that degrades most gracefully.

Perhaps the most pressing – and least glamorous – task lies in developing robust methods for quantifying uncertainty. Point forecasts, however precise, are ultimately meaningless without a clear understanding of their limitations. Intuition suggests the true value of these models won’t be found in predicting what will happen, but in systematically mapping the space of what could happen. Code should be as self-evident as gravity; let the models speak for themselves, without embellishment.

Original article: https://arxiv.org/pdf/2602.05390.pdf

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