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| Yiwen Wang |
| University of Massachusetts Amherst |
Have you ever wondered how power is delivered instantly every time you flip on a light switch? Even if you don’t always know when you’ll need electricity, how do power plants prepare for it? Unlike commodities that can be stored on shelves, electricity must be delivered in real time. Behind the scenes, the grid relies on delicate demand forecasts to make this possible. With rising demand, unpredictable weather, and the growing penetration of renewable energy, one increasingly important tool is machine learning. This article explores how machine learning models are helping the grid enhance the forecasts and keep the lights on.
Why electricity demand forecasts matter?
The power grid is widely considered to be one of the greatest engineering achievements in human history. It is a vast and intricate system that requires sophisticated design and operation, from interconnecting a new project to handling a power outage caused by a thunderstorm. Many of the core challenges come from a basic but critical requirement: electricity demand and supply must be balanced at all times. This balance is made difficult by the inherent uncertainty and variability of electricity usage and generation. A balance disruption can cause severe consequences: an unexpected spike in electricity demand or a sudden shortfall in supply can lead to skyrocketing costs, infrastructure damage, and widespread blackouts that threaten the grid reliability and security (EIA (2021)). An accurate forecast of next-day electricity demand is essential for both the grid operators and individual power plants to plan ahead. It enables them to decide when and which power plants to schedule to minimize the operation costs while ensuring adequate resources. For example, grid operators closely monitor how supply meets demand to maintain the balance. California Independent System Operator publishes the current, day-ahead forecasted, and hour-ahead forecasted demand every five minutes on their website. But how do the forecasts work- and how can we make them more accurate?
How ML enhances Electricity Forecasting?
Electricity demand is influenced by many factors, most notably by the weather. On the demand side, temperature and humidity affect how much heating and cooling are needed by residential and commercial users. On the supply side, solar irradiance and wind speed influence the amount of electricity that can be produced from renewable sources like solar panels and wind turbines. Often treated as zero-cost inputs, these sources contribute directly to meeting demand by offsetting the need for electricity from conventional generation.
Additionally, electricity usage is closely tied to time and calendar data. Customer behavior varies throughout the day and week, but typically peaks in the morning and early evening. Holidays and weekends also bring noticeable shifts in usage patterns.
Traditional forecasting methods can capture the effects of such factors. Time series models can predict recurring trends and seasonality of electricity demand from time series data. Linear regression models estimate the relationships between electricity demand and other affecting features. Rule-based systems use an if-then logic that models customer behaviors. However, these traditional methods struggle with non-linear relationships and large-scale datasets.
As the energy grid evolves with deeper integration of renewable energy and emerging demand types such as data centers, accurate electricity demand prediction is more important than ever, but is also more challenging. Instead of traditional methods, Machine learning (ML) models can be a powerful tool for enhancing the forecast by learning the non-linear patterns between multiple variables patterns and fully utilize large-scale historical data. Models such as neural networks, decision trees, and support vector machines can be great candidates for such applications across both short- and long-term horizons. Long Short-Term Memory (LSTM) models are particularly suitable for modeling long-term time series features. Decision trees and random forests split data into categories, allowing the patterns in different conditions to be assessed separately. K-Nearest Neighbors project the demand by finding similar days from the historical data and use them to forecast today’s demand.
There could be challenges in applying machine learning models to electricity demand forecasting. Advanced ML models may deliver highly accurate forecasts, but the results can be hard to interpret. Grid operators need to understand the results before making decisions. Furthermore, while training ML models with millions of data points can be manageable, scaling the model to a grid system with millions of users that change by the second requires substantial efforts. In addition, how those models can adapt to extreme weather is still an open question. Heatwaves, extreme cold, and hurricanes are rare conditions but critically impact the grid. Those scenarios can be "edge cases" for ML models because they have not appeared frequently in the historical data. Ultimately, we would like to build an accurate and reliable model that can be continuously updated with a fast response.
Next step of enhanced electricity demand forecasts
While an enhanced electricity demand forecast is vital in helping the grid operators with load balancing, it can further support a wide range of grid operations. Grid operators are enabled to deploy renewable integration efficiently, launch demand response programs, and facilitate smart grid optimization. From a power plant’s perspective, more accurate electricity demand forecasts inform their pricing strategies. Understanding the dynamic interplay between supply and demand motivates generators to position themselves in the electricity market strategically for revenue maximization.
Machine learning models offer a powerful approach to enhance electricity demand forecasting. They provide more accurate estimates by modeling the impacts of factors such as weather patterns and customer behaviors. Yet, they also come with challenges in model complexity and scalability, as well as the ability to handle extreme events. Looking ahead, combining different categories of models may be a promising way that offers both accuracy and robustness.
References:
EIA, 2021. Extreme winter weather is disrupting energy supply and demand, particularly in texas. URL: https://www.eia.gov/ todayinenergy/detail.php?id=46836.
Acknowledgments: I would like to thank Justin Dumouchelle and Parsa Nikpour for taking the time to review this article. Photo credit goes to Mohammad Mardani for the header photo.