Why utilities need a new approach to load forecasting
And how impactECI delivers it
This article is a collaborative effort by Peter Hans Hirschboeck, Mark Cira, Brian Lithen, and Lisa Nguyen, representing views from impactECI
The electric grid is facing an unprecedented shift in load dynamics. Rising temperatures, extreme weather, electrification, data centers, and distributed energy resource adoption are rewriting consumption patterns. Yet many utilities still rely on legacy models and historical baselines that no longer hold. The cost of these miscalculations is real: from underinvestment and reliability gaps to rate case rejections and lost stakeholder trust.
impactECI's augmented load forecasting tool addresses this challenge head-on. By integrating econometric modeling, technology-specific adjustments, and scenario-based envelope forecasting, the tool offers a tailored, defensible approach to anticipate future demand, equipping utilities with the foresight required to build resilient, future-ready systems
A new era for electricity demand and a growing gap in forecasting accuracy
U.S. electricity demand is expected to grow at an annual rate of +3.4% through 2040, a sharp reversal from two decades of nearly flat consumption, when demand increased by just +0.1% annually between 2005 and 2020. This growth is not simply additive—it reflects a convergence of deep structural changes that are reshaping how, when, and where electricity is used. Building and transportation electrification is driving new load across both residential and industrial segments, while the rapid expansion of hyperscale and AI data centers could contribute up to 12% of total demand by 2030. At the same time, behind-the-meter solar, battery storage, and distributed energy resources (DERs) are decentralizing supply and redefining net load dynamics. Compounding these shifts, climate change is introducing new volatility, with more frequent and more severe weather events, as well as boosting summer peaks, that challenge both generation capacity and forecast reliability.
Yet while the grid evolves and data centers change the game, many utilities continue to base their demand forecasts on outdated methodologies—typically drawing from historical consumption patterns and linear extrapolations that fail to capture emerging load drivers. The result is a growing incidence of significant forecasting errors, or “big misses,” that carry material financial and operational consequences. Analysis of peak load forecasts across multiple U.S. utilities between 2017 and 2022 revealed deviations exceeding 10–15% in some service territories. These discrepancies have led to under-procured demand response capacity, deferred or misaligned generation and transmission investment, and rate cases that lack sufficient evidentiary support—all compounding the risks to grid reliability, capital efficiency, and customer affordability.
Key challenges: forecasting in an age of uncertainty
The current challenge is not just about accuracy—it’s about adaptability. Traditional load forecasting frameworks are ill-suited to handle today’s level of complexity. They fall short in four key areas:
Inability to account for novel, nonlinear load drivers: EV charging, heat pump adoption, hyperscale and AI compute demand, and onsite solar-storage systems all have complex, nonlinear effects on the grid. Forecasting tools built on aggregate historical demand are unable to model these dynamics.
Exclusion of locational or probabilistic data: Critical loads like hyperscale data centers are often planned far in advance but lack coordination with utilities. Without incorporating planned developments and probabilistic weighting of interconnection queues, forecasts may ignore near-term growth drivers entirely.
Limited climate normalization and weather sensitivity: Climate-adjusted baselining is rarely performed comprehensively—yet extreme heat events are already reducing reserve margins by over 40% in regions like California during peak months.
Reactive rather than anticipatory planning: Utilities frequently wait until observable demand changes materialize before adjusting forecasts—resulting in planning delays, reactive infrastructure deployment, and service strain.
The impactECI approach: layered, transparent, and built for uncertainty
To address these gaps, impactECI has developed a modular, multivariate approach to long-term load forecasting—tailored for utilities operating in a landscape of uncertainty. The platform integrates three core pillars:
Flexible-multivariate econometric load modeling
Technology adjustment modeling
Scenario-based views of uncertainty
Each of the modifications that impactECI made to the traditional load forecasting model addresses the increased uncertainty utility load forecasting faces. These changes are discussed in greater detail below.
1. A flexible-multivariate econometric load modeling:
Rather than relying solely on historical load trends, the model integrates a wide range of time-lagged and real-time variables to build a statistically sound baseline trajectory. These include macroeconomic indicators such as GDP growth, retail activity, and employment by sector; demographic trends including household formation, population growth, and vacancy rates; and dynamic market signals such as energy prices, tariff structures, and incentive programs. While the core econometric model is built on demographic, economic, and weather-normalized historical data, but does not include the effects of technology driven-load modifiers such as Solar, Battery, EV and building electrifications. These technological impacts are first removed from the historical load data to isolate the segment of load that follows traditional economic and demographic dynamics. The econometric model is then applied to this adjusted base.
After establishing the econometric baseline, technology cost curves—covering solar, battery storage, EVs, and building electrification—are integrated as modular adjustment layers, reflecting forward-looking shifts in adoption propensity. These effects are reincorporated to the forecast only after the econometric modeling is complete. By applying optimized regression techniques and calibrating results with localized data and utility-specific validation, the model delivers a granular, defensible forecast that adapts to the unique conditions of each service territory.
2. Technology adjustment modeling - translating emerging trends into demand impact:
To refine forecast accuracy, impactECI integrates a suite of technology-specific modules that isolate the impact of key demand-side transformations often overlooked in traditional models. These modules disaggregate drivers such as electric vehicle adoption—leveraging vehicle registration trends, fuel price sensitivity, and charging behavior patterns—to provide a granular view of transportation electrification. Similarly, behind-the-meter solar and storage adoption is modeled using region-specific policy frameworks, cost trajectories, and household adoption propensities. The platform also accounts for the growing influence of building electrification, incorporating appliance replacement trends, space heating load profiles, and weather-adjusted usage data. Finally, data center growth is addressed through a structured pipeline of announced and active projects, differentiated by type, location, and expected onsite generation. By stripping out historical distortions and re-integrating emerging load contributions through this modular lens, impactECI enables utilities to construct a forward-looking, technology-adjusted demand forecast that aligns with the pace and complexity of grid transformation.
3. A scenario-based view of uncertainty:
Recognizing that effective forecasting must account for both precision and uncertainty, impactECI employs an envelope forecasting framework that equips utilities with a full spectrum of plausible future demand outcomes. This dual-track approach blends quantitative rigor with strategic foresight.
Bounded analysis offers a bottom-up view by stress-testing input variables—such as minimizing EV costs or maximizing DER uptake—to define the outer limits of potential load growth
In parallel, narrative analysis applies a top-down lens, layering in qualitative drivers, such as supply chain disruptions, regulatory shifts, or extreme climate events to construct grounded yet flexible scenarios. Together, these methodologies generate a defensible and transparent forecast range that not only withstands regulatory scrutiny but also enhances cross-functional planning—from integrated resource planning (IRP) to long-term capital investment strategy.
Designed for utility planners and built for those who need to know
Unlike generic forecasting solutions, impactECI’s tool was purpose-built to serve utilities and grid operators—but its transparency, modularity, and locational granularity also make it a powerful tool for investors seeking to quantify market opportunity, and for developers identifying optimal siting for infrastructure. Its design reflects the need for deep customization, allowing forecast inputs to be tailored by geography, customer class, and local technology adoption profiles. The tool fosters collaboration through an interactive modeling process, enabling utility planners to actively select, weight, and validate variables most relevant to their service territories. Importantly, it meets the scrutiny of regulators by offering full transparency—every scenario, assumption, and input is documented and traceable, supporting auditability and stakeholder confidence. Additionally, impactECI integrates advanced climate normalization techniques, stripping out weather-related anomalies from historical data before layering in forward-looking energy trends. This ensures a more stable baseline and a more accurate forecast of emerging load dynamics.
Delivering results: a more accurate, more agile forecast
impactECI’s forecasting approach is already delivering tangible results across multiple utility engagements. The platform also improves alignment with demand-side resources, enabling more cost-effective demand response strategies and optimized program design. In regulatory proceedings, utilities equipped with impactECI’s forecasts have reported enhanced credibility, particularly when seeking approval for infrastructure investments or justifying rate case assumptions. Perhaps most significantly, the platform has strengthened stakeholder confidence by providing transparent, scenario-driven insights that support informed decision-making. As a result, utility planners are no longer reacting to demand shifts—they are proactively managing them.
The path forward: building forecasting resilience for a dynamic grid
The electric grid is not just becoming more digital, decarbonized, and decentralized—it is becoming more uncertain. As utilities face exponential demand growth and rising complexity, the ability to forecast accurately—and credibly—is emerging as a core capability.
impactECI’s Augmented Load Forecasting solution offers a path forward. Built on a foundation of data science, enhanced by domain expertise, and informed by real-world constraints, the platform gives utilities the clarity and confidence to navigate what comes next.
In a world where megawatts translate into millions, and trust hinges on transparency, precision matters more than ever. Forecasting the future isn’t optional. It’s strategic.
For more information, reach out to hello@impactECI.com
The views and opinions in these articles are solely of the authors. They are offered to stimulate thought and discussion and not as legal, financial, accounting, tax or other professional advice or counsel.
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