For energy retailers, success hinges on one particularly critical capability: accurately predicting a customer’s future energy consumption. While billing for past usage is an operational necessity, it's this forward-looking prediction that allows retailers to function effectively as market makers between wholesale generators selling capacity years ahead and individual consumers locking into contracts. This isn't about physically supplying power – other parts of the industry handle that, allowing consumers to switch retailers seamlessly. It’s about mastering the data.

Predicting anything years in advance is challenging enough, but energy markets are a whirlwind of fluctuating weather, evolving policy, shifting consumer behaviours (like the UK's famous kettle surge during TV ad breaks), and disruptive technologies. Fail to predict accurately, and the stakes are immense: retailers have been wiped out by extreme weather like the Texas winter storms or geopolitical price shocks such as the surge in gas prices following the war in Ukraine. Even in more stable times, operating on thin margins means accurate forecasts are non-negotiable for optimal energy purchasing and product pricing.

Adding to this complex picture is the transformative potential of artificial intelligence. The extraordinary growth in AI capabilities could herald a new age of automation and accuracy in energy forecasting, offering solutions to these long-standing challenges.

In today’s post, we’re going to demystify energy forecasting, discuss its challenges, and explore how modern tools can help.

What is energy forecasting?

At its heart, energy forecasting is the science (and art) of predicting future energy needs and market conditions. For an energy retailer, this isn't just about gazing into a crystal ball; it's a fundamental business process that underpins strategic decisions, operational efficiency, and ultimately, profitability. 

Let's break down the key types of forecasting essential for energy retailers:

Volume Forecasting: This is arguably the bedrock of an energy retailer's operations. Volume forecasting involves predicting the total amount of energy that your customers will consume over a specific period. This can range from:

• Short-term forecasts: Looking ahead minutes, hours, or days, crucial for daily balancing in the wholesale market. The growth of renewables has added extra complexity: on the supply side, retailers have to consider intermittent renewable supply, while on the demand side the rise of the prosumer and distributed energy resources complicates demand profiles.
• Medium-term forecasts: Spanning weeks to months, these inform purchasing strategies (hedging) and operational planning.
• Long-term forecasts: Extending from a year to several years, these are vital for strategic decisions like resource planning, infrastructure investment considerations (though more on the grid side, retailers need awareness), and developing long-term customer contracts. Hedging will also take place over these longer time scales.

Why it's vital: Accurate volume forecasts enable retailers to procure the right amount of energy, avoiding the financial penalties of over-or under-buying in volatile wholesale markets. They also help in managing network charges and ensuring supply reliability for customers.

Point-of-Sale (POS) Forecasting: A more specific but increasingly critical type of forecasting, POS forecasting zeroes in on individual customer (or potential customer) energy behaviour, right at the point where a contract or quote is being prepared. It takes historical energy usage data directly from meters (and potentially other sources like solar panel generation data for "prosumers") and creates a long-term "prosumption" forecast – that is, a prediction of both the energy the consumer will use and/or generate. This forecast typically covers the duration for which the retailer wants to provide a quote or close a deal.

Why it's vital: POS forecasting is key to offering competitive and accurate quotes for new contracts, especially for customers with more complex energy profiles like those with solar panels, batteries, or electric vehicles. It allows retailers to tailor offers based on a robust prediction of future individual net consumption, directly impacting sales conversion and the profitability of new acquisitions.

Price Forecasting: This involves predicting future wholesale energy prices. Retailers use these predictions, even if sourced externally or at a higher level, to inform their hedging strategies (when to buy energy for future delivery) and to help structure the pricing of their retail products to consumers.

Why it's vital: Wholesale price movements directly impact a retailer's cost of goods sold. While deep dives into market price modelling are a separate discipline, an awareness of price dynamics is essential context for volume and POS forecasting activities, as they all interconnect in the overall strategy.

Regardless of the specific type, most energy forecasts are influenced by a complex interplay of factors. These typically include:

• Historical Data: Past consumption patterns, previous price movements.
• Weather: Temperature, humidity, cloud cover, wind speed (especially for renewables), and extreme weather events.
• Economic Activity: GDP growth, industrial output, business operating hours.
• Calendar Effects: Day of the week, time of day, public holidays, and seasonality.
• Market Events: Major outages, changes in generation capacity, interconnector flows.
• Emerging Technologies: Adoption rates of electric vehicles (EVs), heat pumps, rooftop solar (photovoltaics or PV), and battery storage.
• Customer Behaviour: Changes in energy usage habits, uptake of energy efficiency measures, participation in demand-response programs.
• Policy and Regulatory Changes: Introduction of new tariffs, subsidies, or market rules.

Understanding these fundamentals and the key drivers is the first step towards appreciating both the challenges inherent in energy forecasting and the immense value that accurate, data-driven predictions can bring to an energy retailer.

Why accurate forecasting is non-negotiable

Not exactly a difficult question to answer, given that we already covered it in the introduction. Thin margins and broad impacts make it one of the core capabilities of any energy retailer. For a bit more detail, here’s how different parts of a retailer can be impacted:

• Procurement: Buying the right amount of energy at the best possible price. In general the further ahead a retailer purchases energy, the better, so having a more accurate idea of baseload requirements will lead to lower costs. 
• Pricing: Developing competitive and profitable retail products. Finding the sweet spot between the most competitive price for a customer with the best margin requires a very effective understanding of the customer's likely demand and the costs of energy.
• Risk Management: Hedging against price volatility and volume uncertainty. Exogenous price shocks can have devastating impacts, but they are rare. Constantly paying hedging costs is a good way to lose money on hedging. You need to forecast such that hedging costs are low but still protect against risk.
• Operational Efficiency: Optimizing resources, reducing imbalance costs. Failing to procure the right amount of energy will incur balance costs, which can be minimized with better forecasts. More generally, the less effort you need to put into forecasting and correcting forecast errors, the more resources you can direct elsewhere.

The Achilles' heel: Common challenges in energy forecasting

While the why of energy forecasting is clear, the how is fraught with challenges. Energy retailers operate in a dynamic and often unforgiving environment. Even with the best intentions, achieving consistently accurate forecasts can feel like trying to hit a moving target in the dark. Several persistent pain points plague traditional forecasting efforts, making them a significant operational headache and a source of considerable financial risk.

Let's explore some of the most common hurdles:

Data dilemmas: Garbage in, garbage out 

Effective forecasting is fundamentally data-driven. However, the quality, accessibility, and sheer volume of data often present major obstacles:

• Siloed and inconsistent data: Information crucial for forecasting (e.g., historical consumption, customer details, meter attributes, weather data) often resides in disparate, unconnected systems.
• Data quality issues: Incomplete records, inaccuracies, missing values, or incorrect timestamps can severely compromise the reliability of any forecast built upon them.
• Integration complexity: Modern retailers need to integrate a growing variety of data sources – from smart meter interval data and weather APIs to market pricing feeds and demographic information. 
• Granularity gaps: Sometimes the data isn't available at the level of detail (e.g., half-hourly vs. daily) needed.

Escalating market volatility and uncertainty 

The energy landscape is more volatile than ever before, making historical patterns less reliable predictors of future behaviour:

• The renewable revolution: The increasing penetration of intermittent renewable energy sources has introduced significant variability.
• Geopolitical instability: Sudden and dramatic shifts in fuel prices and energy availability have arisen in recent years.
• Extreme weather events: More frequent and intense weather events (heatwaves, cold snaps, storms) can cause unprecedented spikes or drops in energy demand.

Shifting consumption patterns 

The way consumers use energy is no longer static, driven by new technologies and changing behaviours:

• The rise of DERs: Increased adoption of DERs, EVs, and heat pumps is transforming traditional demand profiles. 
• Changing lifestyles and work patterns: Shifts towards remote working, adoption of smart home devices, and general energy awareness initiatives can subtly but significantly alter aggregate consumption patterns over time.

Model limitations

The models and methodologies used for forecasting can themselves be a source of weakness:

• Outdated or simplistic technology: Many retailers still rely on relatively basic platforms, sometimes even spreadsheet-based, which struggle to incorporate the vast array of influencing factors.
• Inflexibility: Traditional tools may be slow to adapt to structural breaks or new emerging trends in the data, leading to deteriorating forecast accuracy over time.
• "Black Box" syndromes: Some advanced models can be difficult to interpret, making it hard to understand why a model is producing a certain prediction.

Process and resource inefficiencies 

Beyond the data and models, the human and process elements can also hinder forecasting effectiveness:

• Manual and time-consuming processes: Significant manual effort is often involved in data gathering, cleaning, model running, and report generation, diverting attention from value-add activities.
• Scalability challenges: As a retailer's customer base grows or as data granularity increases (e.g., with smart meter rollouts), existing forecasting processes and systems may struggle to cope with the increased load.
• Slow feedback loops: The process of evaluating forecast accuracy and feeding those learnings back into model improvements can be slow and cumbersome.

Navigating these challenges requires more than just incremental improvements. It demands a fundamental rethinking of the tools and processes that underpin energy forecasting. Only then can retailers hope to transform this "Achilles' heel" into a source of competitive advantage.

The Gorilla solution: Powering smarter energy forecasts

Over the years since energy markets liberalized, energy retailers have got quite good at doing forecasting. There’s always a temptation with new products to declare “Throw away your old methods, it’s time for something completely new!”. But this is not the case for forecasting; the core models and algorithms do work. What is needed from a tool is efficiency and scalability for the modern environment, rather than throwing the baby out with the bath water. 

For Gorilla, addressing the needs of forecasting teams means a focus on three key capabilities: automation, flexibility, and granularity. In a sense, the key advantage Gorilla brings isn’t anything related to forecasting but is all about data provided by the Gorilla data cloud. It’s the data cloud that centralizes operational, market, and financial data while acting as a single point for all integrations. The days of wasting time on manual data cleaning and ingestion are long gone. The ability to handle vast volumes of data and scale up to the needs of each retailer are not possible without Gorilla’s platform.

At the same time, our forecasting application can run as often as you need, delivering results at pace. Adapting your model and inputs is made easy. We’re not here to “revolutionize” the way energy retailers do forecasting. It’s just about making every part of the process that much better.

Conclusion: Forecasting the future, confidently

You won’t need to be a genius forecaster to predict the content we’ve discussed today. Forecasting has been one of the key differentiators for energy retailers for many years, and that is only going to continue. Increasing volatility and the pace of change will ensure that staying one step ahead of your competitors when it comes to forecasting ability will be vital.

The good news is that there are more solutions to these challenges. Modern tools and technology, including artificial intelligence, will open up new avenues for more accurate, more flexible, and faster predictions. 

However, even the best applications can’t work in a vacuum. At Gorilla we believe that the essential step towards better forecasting is taking control of your data. Eliminating silos and integrating disparate sources will transform the resources available to a forecasting application. That’s why our forecasting is built on a holistic approach, with the Gorilla data cloud providing the underpinning to our forecasting applications. If you’re ready to find out more, request a demo today.