The Future of Energy Forecasting: How Deep Learning is Shaping Predictions in the Power Sector
Over the years, forecasting methodologies have advanced dramatically, from traditional statistical models to sophisticated AI-driven techniques. This series of articles will explore the evolution of forecasting approaches, beginning with classical models like ARIMA, advancing through machine learning methods, and culminating in state-of-the-art deep learning approaches such as transformers and Mixture-of-Experts.
At TokWise, we conduct extensive experimentation with these methods on real-world energy data.
In this series, we’ll not only break down each approach but also discuss their relevance, strengths, and limitations in forecasting energy signals, drawing from our own findings and industry insights. Whether you’re a data scientist, energy analyst, or curious reader, this journey will provide you with a comprehensive understanding of how forecasting has evolved and what methods are most effective in today’s energy landscape.
Accurate forecasting is essential in energy markets, where even small fluctuations in supply or demand can have substantial financial implications. As the sector evolves with the growing use of renewables, the demand for advanced forecasting tools has never been greater. Traditional statistical methods, while efficient, often struggle with the increasing complexity of energy data. In contrast, machine learning (ML) and deep learning (DL) models offer exciting new possibilities.
A 2022 study titled "Statistical, Machine Learning and Deep Learning Forecasting Methods: Comparisons and Ways Forward" sheds light on how DL methods stack up against traditional statistical and ML techniques in time series forecasting. The study found that, while statistical models (such as ARIMA, SARIMA, and exponential smoothing)? often excel in short-term predictions, deep learning models outperform them when forecasting across multiple steps ahead. This is particularly significant for energy markets, where multi-step forecasts (e.g., predicting all Day-Ahead prices for the next day) are crucial for managing volatility in prices and supply.
In this post, we’ll explore how these advanced forecasting techniques could change the game for the energy sector.
1. The Traditional Approach: Statistical Forecasting in Energy
For decades, participants in the electricity trading markets have relied on statistical methods to forecast electricity demand, price fluctuations, and generation capacity. These models excel in capturing linear and seasonal trends, such as daily price cycles or demand patterns driven by weather or economic factors. In electricity markets where power is bought and sold in real-time or through contracts, these methods have been instrumental in helping market participants anticipate changes in supply and demand, manage risk and optimize trading strategies
Statistical models are well-suited for situations where the data follows predictable trends, such as energy demand increasing during peak times and dropping at night. These methods are straightforward to implement and computationally efficient, making them ideal for day-to-day energy forecasting where quick decisions are needed.
However, as energy systems become more complex—integrating renewables, managing smart grids, and accounting for unpredictable weather events—traditional methods often fall short. They struggle to capture the non-linear, dynamic relationships between variables like weather patterns, consumer behavior, and grid performance.
2. Enter Machine Learning and Deep Learning: A New Era of Energy Forecasting
Machine learning has made significant inroads in various industries, and the energy sector is no exception. Unlike statistical models that require pre-defined assumptions about the data (like trend, seasonality, and stationarity), ML methods (such as gradient-boosted trees, ?k-nearest neighbors, etc) can learn complex patterns directly from the data. This allows them to integrate more intricate data sources such as solar and wind energy output, which is notoriously volatile.
Deep learning, a subset of machine learning, has pushed the boundaries even further. In the study by Spyros Makridakis and his colleagues, DL methods were shown to outperform both statistical and traditional ML methods in many forecasting scenarios, especially when predicting multiple steps in the future.
For energy companies, this could translate to more accurate forecasts of energy prices on different markets such as Day-Ahead, Intra-Day, and balancing —an area where traditional methods often struggle.
For example, DL models can process vast amounts of historical data, including weather conditions, past energy prices, and grid data, to forecast energy future prices with high accuracy. They excel at capturing complex relationships, such as how a sudden drop in wind speed could reduce power output from wind farms, leading to the need for supplemental power from other sources.
3. Performance Metrics
The table above compares the performance of various forecasting methods across different time horizons (short, medium, long) using two key metrics:
The different forecasting methods tested are as follows:
Observations:
Improvement of Ensemble-DL over Ensemble-S:
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4. The Limitations: Not All Deep Learning Models Are Created Equal
The Deep Learning models used in the study are not considered state-of-the-art by today’s standards. The best-performing DL model in the study, Amazon’s DeepAR, managed to outperform statistical models at an individual level.
However, DeepAR is more than six years old. While still relevant, it is far from the cutting edge in terms of modern DL models for forecasting. The study shows that while newer DL architectures are likely to offer even greater improvements in forecasting accuracy even models like DeepAR and N-Beats can outperform statistical approaches.
For the energy sector, this highlights the importance of keeping up with advancements in AI and DL technologies. Relying on outdated models could mean missing out on the full potential of deep learning when participating in electricity markets.
5. The Trade-off: Computational Power vs. Forecasting Power
In the study, deep learning models like DeepAR and WaveNet outperformed statistical models in long-term electricity market forecasting but required significantly more computational power and time to train. These DL models are well-suited for complex, high-stakes scenarios, such as multi-step price forecasting and managing volatility. However, they may be overkill for straightforward, short-term predictions, where statistical models can often deliver adequate performance with far less computational effort, making them more practical for less complex trading strategies.
6. The Battle of Horizons: Statistical Models vs. Deep Learning
When it comes to time series forecasting, there are three key observations to consider:
This is particularly relevant for electricity market participants who need to forecast prices or supply conditions at frequent intervals—such as every 15 minutes or every hour. Using deep learning models, participants can generate more accurate multi-step forecasts, allowing them to minimize errors over extended forecasting horizons.
This leads to better market positioning and decision-making in volatile trading environments, helping participants manage risks and maximize returns across complex, fluctuating market conditions.
7. How Data Affects Model Performance: Deep Learning vs. Statistical Model
A crucial observation from the study is that as the dataset size increases, the performance gap between deep learning (DL) and statistical models narrows.
Initially, using just 1,045 series from the M3 dataset, the DL ensemble outperformed the statistical models after the first few horizons. However, when the full dataset of 3,003 series was analyzed, the gap between Ensemble-DL and Ensemble-S became smaller.
Statistical models matched the DL models in the first horizon, but as the forecasting steps increased, DL models continued to excel, specifically showing improvement with more complex, noisy, and non-linear data.
This performance boost is not just a reflection of the model type but also the nature of the data. The study identified that DL models handle noisy, trended, and non-linear data more effectively, while statistical models are more suited for seasonal and low-variance data.
This insight is crucial for electricity market forecasting, where datasets can vary significantly depending on the target—whether it’s volatile price movements, supply fluctuations, or more predictable trends. Understanding these data characteristics helps market participants choose the right model to optimize their trading strategies.
8. Tokwise: Using State-of-the-Art Neural Networks for Smarter Electricity Trading
Developing, implementing, and maintaining deep learning models for energy market forecasting requires a workforce skilled in data science, machine learning, and energy domain knowledge.
At TokWise, we focus on leveraging state-of-the-art neural network approaches to power our advanced trading strategies, delivering top-tier forecasting solutions to electricity market participants. Our sophisticated models generate real-time signals that help clients optimize their trading decisions, manage risk, and capitalize on market fluctuations.
By identifying complex patterns in data, we provide actionable insights that enhance profitability and give traders a competitive edge in volatile electricity markets. At Tokwise, innovation and precision are at the heart of our approach to smarter trading.
9. Conclusion: Embracing DL for a More Profitable and Risk-Managed Trading Approach
The future of electricity markets is closely tied to advancements in machine learning and deep learning. As market dynamics grow increasingly complex, traditional methods will no longer be sufficient to manage the volatility and risk associated with real-time trading.
Deep learning offers more accurate, long-term predictions that can help market participants capitalize on price fluctuations and optimize trading strategies.
By combining the strengths of statistical and deep learning models, participants can improve forecasting precision, reduce financial risk, and maximize profitability in an evolving marketplace.
The article was written by Alex Nedyalkov . At Tokwise, he leads the development of advanced analytical solutions, focusing on researching and applying AI approaches to enhance the company's modeling capabilities.