Why utility-scale solar requires a smarter approach to predictive modeling

The Solar Energy Industries Association reports that Texas installed the most solar capacity in the first quarter of 2025 nationally, followed by Florida. This growth can largely be attributed to utility-scale solar projects, and represents a national trend.

As utilities manage increasingly large portfolios of renewable energy, the financial risks of inaccurate forecasting become amplified and impossible to ignore. Forecasting errors can lead to overproduction or underproduction, forcing operators to incur losses by either selling excess energy at a lower price, or buying additional energy at a higher price. In worst-case scenarios, shortfalls can trigger costly penalties, which can further compound losses.

By supplementing or upgrading traditionally manual forecasting processes with state-of-the-art digital tools, grid operators and utilities can make better decisions faster across planning, procurement and real-time operations, to avoid overproduction, overpaying, or compromising reliability. In short, making utility forecasting more accurate, real-time, and agile is good for business. However, achieving that level of precision requires accounting for an increasingly complex energy landscape, one in which the variables extend far beyond traditional weather patterns or irradiance forecasts.

As an intermittent energy resource, solar generation faces challenges beyond the weather and changes in irradiance alone. The broader shift toward distributed energy resources means utilities are contending with a much more dynamic environment. In addition to utility-scale DER generation, the energy business today includes residential rooftop solar generation, residential battery storage, demand from an increasing amount of electric vehicles, and demand-side flexibility programs, each with unique data signals, constraints and impacts on the grid.

Forecasting in this environment is no longer about projections for a single data stream. Utilities must now anticipate how renewable production intersects with fluctuating customer demand, infrastructure limitations, regulatory reporting requirements and exposure to increasingly volatile energy markets. This means looking at both sides of the equation; predicting not just how much renewable energy will be produced, but also the anticipated load from their customer base. With high-fidelity predictions, utilities can more effectively “meet in the middle” to drive financial efficiency.

Many utilities, however, still rely on spreadsheets or homegrown tools maintained by a small group of domain experts in the back office. These methods, while proven over decades, simply cannot keep up with the speed and complexity of modern grid operations. There’s too much data, too much variability, too little time and too much at stake.

That’s why we now see broader adoption of forecasting technologies that use machine learning, probabilistic modeling and artificial intelligence. Such tools allow operators to run thousands of what-if scenarios, adapt to real-time conditions in near real-time and make more informed and risk-aware decisions.

With a smarter approach to predictive modeling, utilities will have significantly more confidence in the gambles and bets they need to make for tomorrow’s market, allowing them to reduce costs, get the most use out of their PV assets and even sell excess generation back at higher rates when demand is right. As such, a customer could reduce imbalance fees from $14 million to $10 million (nearly 30% savings) with just a 2% improvement in forecasting precision. Rather than react to errors after they happen, utilities can proactively manage the likelihood of shortfalls or overproduction in advance, and respond accordingly. And the benefits of these advanced forecasting capabilities are not just theoretical anymore because these tools are already delivering measurable results for utilities and energy providers in the field.

One example of how better forecasting drives sustainability comes from a retail power provider serving large commercial customers, including a national chain of convenience stores. For years, this customer had been purchasing clean energy to improve their sustainability performance. However, this company’s emissions reporting relied exclusively on generalized information and estimates from the energy provider, limiting their ability to provide a detailed and data-backed view of the impact those investments made.

By integrating a forecasting platform capable of modeling distributed solar generation at a granular level, asset by asset, the retail power provider could match solar energy forecasts with each customer’s specific usage profile. This pairing gave the convenience store operator a precise understanding of where their power was coming from and how much clean energy they were actually using, right down to the hour.

Instead of relying on broad percentages, the customer could now generate sustainability reports grounded in real production and consumption data. This unlocks a new level of credibility and clarity for their environmental performance metrics, which were especially important for internal benchmarks and public sustainability disclosures.

For utilities and energy providers, delivering this kind of transparency isn’t just about customer satisfaction, it’s also about market differentiation. As demand for clean energy accountability increases, those who can verify performance in real-time will lead. Meeting these rising expectations requires tools capable of processing massive, fast-changing data streams and turning them into actionable insights.

The confluence of intermittent DERs, unpredictable weather, volatile energy markets and ever-shifting demand patterns create a data set too large and complex for humans to manage efficiently. The spreadsheet wranglers at headquarters need the power of smarter, more responsive approaches to forecasting. Utilities that acknowledge that their financial health increasingly depends on how well they manage these uncertainties become more efficient.

Like any other business, the top priority of a utility is to remain financially viable. Today, that means having the ability to ingest, process and act on information distilled from massive volumes of operational and market data, constantly and with minimal latency. This level of responsiveness requires new tools in the hands of planners and managers.

Fortunately, the technology now exists to leave legacy tools and chronic uncertainty behind. Software platforms built specifically for the energy sector are enabling a new standard of forecasting precision, agility and confidence. With these tools in hand, utilities can stop chasing the future, and start predicting it with clarity.