How Power Suppliers Can Stay Ahead Even in the Most Volatile Markets

​​For power suppliers, market volatility is a persistent challenge and it’s especially top of mind in our current climate. Extreme weather events are increasing in both frequency and intensity, traditional forecasting methods are proving inadequate. The recent arctic blast in the Northeast was a harsh reminder of the stakes involved. When temperatures plummeted, demand surged, and suppliers without robust forecasting models were left scrambling—forced to either absorb massive financial losses or pass unpredictable costs onto customers.

This is not an isolated incident. Since 2000, weather-related disruptions have accounted for 80% of major U.S. power outages. In 2024 alone, the financial impact of extreme weather on the energy market has been substantial, with billion-dollar disasters becoming more common. With this volatility, suppliers must move beyond reactive strategies and adopt forward-looking forecasting models to maintain stability and profitability.

The Inadequacy of Traditional Procurement Strategies

Energy procurement is a delicate balancing act. While real-time markets may offer lower prices at times, their volatility makes them an unsustainable primary strategy. The day-ahead market provides a degree of stability but still carries price risks, particularly during extreme weather events. Forward markets offer the most predictability, yet they require long-term forecasting accuracy to avoid over- or under-hedging.

The data we tracked during the January freeze revealed substantial price spreads, ranging from minimal differences of a few dollars per megawatt hour to more than $100. This kind of disparity underscores the risks associated with relying solely on market timing rather than leveraging precision forecasting. Each regional energy market presents its own set of challenges:

• ERCOT (Texas): Suppliers can take hedges to real-time settlement, leveraging smart meter data for more accurate forecasting.
• PJM & ISO New England: These markets primarily settle against day-ahead prices, but ISO New England faces additional forecasting challenges due to restricted access to generation data.
• Texas: Smart meter infrastructure offers near-real-time consumption data, an advantage that competitors in other regions often lack.

Data-driven Forecasting is the Key to Stability

Accurate forecasting begins with high-quality data. Power suppliers that prioritize data integrity gain a significant competitive advantage, as complete and reliable customer information directly impacts forecasting precision. Without a strong foundation of accurate data, even the most advanced predictive models will fall short.

At its core, data-driven forecasting relies on two foundational components:

1. A validated and up-to-date count of customer accounts. This ensures all active accounts are properly recorded allowing suppliers to establish accurate load profiles and assess total energy demand effectively.
2. Historical usage patterns that accurately reflect demand trends. By analyzing past energy consumption data, suppliers can identify seasonal patterns, peak usage periods, and fluctuations influenced by economic or environmental factors.

Strengthening Data Integrity

Leading suppliers are now employing specialized teams solely focused on data integrity. These professionals work closely with billing system vendors and continuously monitor incoming data streams to detect and resolve anomalies that could compromise forecast accuracy. The role of these teams is critical in minimizing errors caused by incorrect meter readings, outdated customer records, or missing usage data.

Additionally, energy suppliers must implement rigorous data validation protocols, including:

• Automated data audits: These systems continuously scan data inputs for inconsistencies, flagging missing or incorrect values before they impact forecasting models.
• Cross-checking multiple data sources: Combining smart meter data, weather patterns, and regional grid demand information provides a more comprehensive view of expected energy consumption.
• Human oversight and AI integration: While automated tools enhance efficiency, human experts are essential for fine-tuning forecasts and interpreting irregularities that algorithms may miss.

The Role of Smart Meter Infrastructure in Forecasting

Smart meter infrastructure has become a game-changer in energy forecasting. These devices provide real-time consumption data, allowing suppliers to respond to demand shifts much faster than traditional data collection methods.

For example, in Texas, suppliers can access Smart Meter Texas data to anticipate demand fluctuations more accurately. This offers a substantial timing advantage over competitors who rely on manual ERCOT data reviews. During extreme weather events, real-time access to consumption patterns can be the difference between financial stability and turmoil.

Expanding Beyond Basic Consumption Data

While smart meters provide invaluable insights into short-term demand fluctuations, effective forecasting requires a broader perspective. Beyond raw consumption data, suppliers must track:

• Customer migration between utilities: Shifts in the customer base impact overall energy load and must be accounted for in forecasting models.
• Rate class changes: When customers move between residential, commercial, and industrial rate classes, their usage patterns can shift dramatically, altering demand projections.
• Contract transitions and expirations: Understanding when customers enter or exit contracts ensures that forecasting models account for fluctuations in overall energy demand.

By leveraging these insights, suppliers can make informed hedging decisions, reducing risk exposure and securing long-term price stability.

Keeping Advanced Hedging Strategies for Top-of-Mind

Effective hedging is the most powerful tool a supplier has at their disposal for protecting margins during volatile market conditions. Suppliers that excel at forecasting can optimize their power purchasing strategies, reducing dependence on high-risk real-time pricing. A robust hedging strategy should include:

• Forward energy market procurement: Locking in prices in advance to hedge against extreme weather fluctuations.
• Diversified procurement: Using a mix of real-time, day-ahead, and forward markets to mitigate exposure to sudden price swings.
• Customer rate optimization: Providing fixed-price contracts based on precise demand predictions, allowing for better pricing stability.

During weather-driven disruptions, suppliers with strong hedging practices can continue offering competitive fixed-rate contracts while maintaining healthy profit margins. This not only protects the supplier but also strengthens customer trust and satisfaction.

Leveraging AI and Machine Learning in Forecasting

Advancements in artificial intelligence (AI) and machine learning have transformed demand forecasting, enabling suppliers to analyze vast amounts of historical and real-time data with a level of accuracy and efficiency they simply couldn’t have achieved before. The key benefits include:

• Pattern recognition: Identifying seasonal trends and demand spikes more effectively than traditional models.
• Predictive analytics: Anticipating demand fluctuations based on weather forecasts, economic factors, and regional consumption patterns.
• Automated adjustments: AI-powered systems can continuously refine forecasts, adjusting procurement strategies in real time.

By integrating AI into their forecasting processes, suppliers can significantly enhance their ability to predict demand shifts, optimize hedging, and reduce exposure to market volatility.

Ensuring Long-Term Success in a Changing Market

Accurate forecasting is no longer just a competitive advantage—it is a fundamental requirement for long-term success in the energy industry. Suppliers that invest in data integrity, advanced forecasting models, and strategic hedging practices will be best positioned to navigate an increasingly complex and unpredictable market.

If you want to strengthen your forecasting capabilities, start focusing on these four areas:

• Prioritize data quality: A strong foundation of clean, reliable data is essential for accurate forecasting.
• Diversify procurement strategies: Relying solely on real-time markets is too risky; a balanced approach ensures greater stability.
• Leverage smart meter infrastructure: Real-time consumption data is a game-changer for accurate demand predictions.
• Adopt AI and machine learning: Advanced analytics provide a deeper, more precise understanding of market trends.

With extreme weather events on the rise and regulatory landscapes shifting, the ability to anticipate demand fluctuations will define the next generation of successful energy suppliers. Those who proactively invest in forecasting technology and market intelligence today will be the ones leading the industry into the future.

If you’re looking for both a partner and the tools to help you take your forecasting abilities to the next level, we’d love to show you what Risk360 can do. Book a free demo today.