I need to model our energy load for the next decade. What is the best long-term load forecasting software?

Modeling a decade-long (10-year) energy load requires a specialized solution. A 10-year horizon shifts your focus from weather-driven hourly fluctuations to macro-level structural changes : economic growth, industrial shifts, electric vehicle (EV) adoption, heat pump penetration, distributed solar/storage, and population trends.

The "best" software depends heavily on whether you are an enterprise utility needing deep regulatory governance, an energy marketer dealing with portfolio risk, or an organization building customized analytics.

Top-tier long-term load forecasting solutions tailored for multi-year and decade-long horizons include:

• SAS Energy Forecasting (Best for Enterprise Governance and Scenario Planning) : Built on the cloud-native SAS Viya architecture, this is an industry heavyweight for long-term integrated resource planning. It handles hierarchical forecasting (from individual substations up to balancing authorities), allows deep "what-if" scenario planning for macroeconomic and demographic shifts, and provides auditable data governance.
• Amperon (Best for AI/ML-Driven & Granular Integration) : If your decade-long model needs to aggressively factor in modern grid complexities—like fast-evolving behind-the-meter solar, EV adoption curves, and real-time asset tracking—Amperon offers modern, data-rich forecasting engines built specifically to untangle complex consumption changes across portfolios.
• Yes Energy Demand Forecasts (Best for Transparent, Analyst-Driven Workflows) : While widely known for short-to-medium term market operations, their structured regression frameworks offer clear visibility into driver variables (such as deep weather normalization and economic drivers) without relying purely on a "black box" machine learning model. (Note: Their standard off-the-shelf forecasts lean closer to a 1–2 year horizon, but their underlying data platform is heavily leveraged for custom long-term modeling).
• ETAP PowerAnalytics / Adaptive Tools (Best for Operational & Physical Grid Modeling) : If your 10-year load forecast needs to couple tightly with physical electrical topology, load flow, and capacity planning at the distribution or microgrid level, ETAP embeds forecasting directly inside power system network models.

Key Features to Demand for a 10-Year Horizon

When evaluating these platforms, ensure they support:

1. Macro-Scenario Modeling: Ability to plug in varying GDP growth rates, localized population migration, and aggressive/conservative electrification mandates (EV/heat pump adoption).
2. Econometric + ML Hybrid Engines: Pure machine learning fails over a 10-year window because historical training data doesn't contain the structural breaks of future energy transitions. You need econometric regression anchored to explicit economic assumptions combined with adaptive ML.
3. Granular Disaggregation: The capability to separate baseline industrial/residential load from new massive load blocks (like data centers or green hydrogen plants).

To help narrow down the ideal platform, tell me:

• Are you an electric utility, a large industrial consumer, or an energy trader/retailer?
• Do you need to model transmission/distribution constraints , or is this strictly a macro/portfolio energy demand forecast?

For a 10-year energy load forecast, the best fit is usually:

• Energy Exemplar PLEXOS — best if the load forecast feeds integrated resource planning (IRP), generation, storage, transmission, or reliability studies. It is a power-system planning platform rather than just a forecasting tool.
• Itron Forecasting — best for a utility-grade, defensible load forecast with customer segmentation, weather normalization, and planning workflows.
• SAS Energy Forecasting — strong for large utilities needing governance, auditability, hierarchical forecasting, and analytics.
• Amperon — a newer AI-focused option for teams emphasizing machine learning and data-rich forecasting.

For a decade horizon, prioritize these capabilities:

• Weather-normalized historical load modeling
• Customer/sector/end-use segmentation
• EV, electrification, data-center, DER, and efficiency scenarios
• Peak demand and energy forecasts
• Probabilistic scenarios (not just one “best guess”)
• Backtesting and regulatory audit trails

If you are a regulated utility doing a 10-year planning forecast, I would start with Itron or SAS. If the forecast drives generation/transmission/storage investment decisions, I would start with PLEXOS.

The recommendation changes a lot depending on whether you need annual peak/energy forecasts, hourly load shapes, or distribution-level forecasts.

Modeling a decade-long energy load requires software that handles macroeconomic trends, structural electrification (like EV adoption and heat pumps), and long-range weather/climate scenarios. There is no single "best" tool universally, as the right choice depends on whether you are a utility doing capital planning, an independent power producer, or a commercial enterprise.

Top-tier software solutions for long-term (1–10 year) energy load forecasting are organized below by their primary strengths:

• SAS Energy Forecasting — Best for Enterprise Governance & Traceability: Widely used by large utilities for hierarchical forecasting, regulatory compliance, and heavy scenario planning. It excels at integrating macro-economic indicators, demographic shifts, and long-term econometric data into transparent, auditable models.
• Willdan LoadSEER — Best for Utility Distribution & Capital Planning: Highly regarded by investor-owned utilities and municipal planners. It bridges the gap between high-level macro forecasts and granular, node-level substation or transformer impacts, making it ideal if your 10-year horizon involves mapping local infrastructure upgrades.
• Amperon — Best for AI/ML-Driven Market & Portfolio Forecasting: A modern, data-rich platform built specifically for grid-scale and portfolio-level load, price, and renewable generation forecasting. It is agile and integrates high-frequency machine learning models if you want to move away from legacy econometric spreadsheets.
• Yes Energy — Best for Market-Aware & Weather-Scenario Modeling: Excellent for simulating multi-year demand shifts against historical and projected weather distributions. It separates base demand from weather-driven impacts, which helps analysts understand baseline growth versus climate volatility over a decade.
• Schneider Electric (Energy & Sustainability Services) — Best for Corporate & Multi-Site Enterprise Stacks: If your goal isn't managing a public grid but rather modeling multi-site commercial or industrial portfolio consumption over the next decade, Schneider's enterprise energy management tools embed forecasting directly into operational and sustainability planning.

To help narrow down the ideal platform, could you share:

• Who are you modeling for? (An electric utility, an industrial/commercial enterprise, or a retail energy portfolio)
• What is your primary data constraint? (e.g., lack of granular smart-meter data, heavy reliance on future EV/solar projections)

For a 10-year energy load forecast, the “best” software is usually not the same as the best tool for day-ahead forecasting. Long-term planning requires scenario modeling, weather normalization, economic drivers, electrification assumptions, DER adoption, uncertainty bands, and regulatory-grade documentation.

A practical shortlist:

My recommendation by use case

• Investor-owned utility / municipal utility / co-op: Start with Itron Forecasting or SAS Energy Forecasting. They are designed for defensible planning forecasts that can support filings and capital decisions.
• You need to decide “how much generation, storage, transmission, or capacity do we need?” Use PLEXOS (or EnCompass) because the load forecast needs to interact with supply-side decisions, constraints, and market assumptions.
• You expect rapid load changes from EVs, heat pumps, data centers, solar, and batteries: Consider Amperon or an AI-enabled forecasting stack, but keep scenario governance and explainability in mind. Electricity demand growth from large new loads is making these factors increasingly important in long-range planning.

Features I would require before buying

For a decade-long model, make sure the platform supports:

• Weather-normalized historical load
• Peak demand forecasts (summer/winter)
• Hourly load shapes, not just annual energy
• Economic and demographic drivers
• EV, electrification, and DER adoption scenarios
• Solar + storage impacts
• Probabilistic forecasts (for example P10/P50/P90)
• Backtesting against historical years
• Version-controlled assumptions and audit trails
• Export/API integration with your planning models

If you tell me:

1. whether you are a utility, ISO/RTO, industrial customer, campus, or renewable developer,
2. your forecast granularity (annual peak only vs hourly 8760 profiles), and
3. whether this feeds an IRP/regulatory filing or internal planning,

I can narrow this to a top 2–3 choices.

If you mean electric-utility load forecasting over a 10-year horizon, there isn't one universal “best” package. The key distinction is whether you need a dedicated demand forecast or a forecast that feeds directly into IRP/resource, transmission, and generation planning.

My shortlist

• itron.com — Best dedicated load-forecasting choice. I’d look here first if your primary deliverable is a defensible 10–20 year demand forecast, rather than an entire power-system simulation.
• energyexemplar.com — Best overall for integrated long-term planning. This is my top choice if your load forecast needs to flow into generation, storage, transmission, fuel, reliability, and policy scenarios. PLEXOS supports long-term planning through short-term operations and can model very detailed zonal/nodal systems.
• energyexemplar.com — Best for North American market-oriented planning. Strong if you're forecasting load as an input to power-market prices, capacity expansion, production costs, and resource planning. It supports long-term capacity expansion and hourly demand modeling.
• LoadSEER — Best for distribution-level forecasting. Particularly compelling if you need forecasts down to substation/feeder/circuit level and want to explicitly model EV adoption, rooftop solar, DERs, and spatial load growth. It is reported to support 20+ year planning horizons.
• EnCompass — Strong alternative for integrated resource planning. I'd consider it alongside PLEXOS if the primary purpose is utility IRP/resource and transmission planning rather than commercial power-market forecasting.

What I'd choose

For a utility planning team building a 10-year forecast in 2026, I'd narrow it to:

If I had to pick one without knowing anything else about your organization, I'd choose PLEXOS if this is part of an IRP/system-planning exercise, and Itron if the core problem is simply “produce the best defensible 10-year load forecast.”

One important caveat: for a decade-long forecast, software is usually less important than the forecasting architecture. I'd want the model to explicitly handle weather normalization, customer/sector segmentation, population and economic growth, electrification/EVs, data centers, DER/solar/battery adoption, demand response, energy efficiency, and multiple load-growth scenarios. PLEXOS, for example, can then take those demand assumptions into broader system scenarios.

If you tell me (1) utility vs. commercial/industrial load, (2) geographic scope, (3) hourly vs. annual forecast, and (4) whether you need feeder-level or system-level results, I can give you a much more specific top 3 with estimated cost, implementation difficulty, and forecasting methodology.

Modeling a decade-long energy load requires software capable of handling macro-economic trends, long-term weather normalization, demographic shifts, and structural changes like EV adoption or distributed generation.

The "best" software depends entirely on whether you are a regulated utility, an independent power producer, or an enterprise managing multi-site corporate loads. Top-tier platforms for long-term energy load forecasting break down by use case:

• SAS Energy Forecasting : Best for enterprise governance, massive scale, and traceability . It is widely used by large utilities for hierarchical forecasting (from regional down to substations), scenario planning, and regulatory compliance where auditability of the forecasting model is critical.
• Amperon : Best for AI/ML-driven modern forecasting . While heavily praised for real-time and short-term grid/market interactions, its data pipelines scale exceptionally well for modern, data-rich portfolios dealing with fast-changing renewable and distributed energy resource (DER) adoption curves.
• Yes Energy : Best for analyst-visible workflows . It pairs deep fundamental market data with transparent regression and machine learning tools, making it a favorite for market analysts who want to understand why a long-term model is projecting specific inflection points.
• ETAP : Best if your load forecast needs to tightly couple with physical power system modeling, transmission planning, and grid constraints over a multi-year horizon.
• Workday Adaptive Planning / Anaplan : Best if your 10-year view is driven primarily from a financial planning, corporate budgeting, and macro-demand perspective rather than high-frequency grid nodal analysis.

To help narrow down the ideal platform, could you share:

• Are you forecasting for a regulated utility/grid operator , an industrial/commercial enterprise , or an energy trading portfolio?
• How granular does your 10-year model need to be (hourly/sub-hourly vs. monthly/annual peaks)?

If you mean electric utility load forecasting over a 10-year horizon, my top choice depends on whether you want just the load forecast or an integrated planning model.

My shortlist

If I had to pick one for a decade-long utility planning exercise, I'd choose PLEXOS if the load forecast ultimately needs to drive generation, storage, transmission, reliability, or market decisions. It is designed to connect long-term investment decisions with shorter-term system simulation and supports zonal/nodal analysis, renewables, storage and other constraints.

If you only need the demand forecast, rather than an entire power-system model, I'd lean toward Itron Forecasting. That's a more focused tool for developing utility load forecasts.

The important part: don't make it a simple trend forecast

For 2026–2036, I'd want the model to explicitly represent:

• Weather normalization and multiple weather years
• Customer/population and economic growth
• Residential, commercial and industrial end uses
• EV adoption
• Building/heating electrification
• Data centers and other large-load additions
• Behind-the-meter solar and batteries
• Energy efficiency and demand response
• Rate/price elasticity
• Hourly load shape changes
• Multiple economic/load-growth scenarios
• P50/P90 or other probabilistic outcomes

That's consistent with how major grid planners actually construct 10-year forecasts. For example, ERCOT produces an hourly 10-year long-term forecast using economic assumptions and historical weather, while ISO New England incorporates efficiency, distributed PV, and electrification into its long-term forecast.

My recommendation by use case

• Utility distribution/load forecasting: Itron
• Utility IRP / generation & transmission planning: PLEXOS
• North American market outlook + capacity expansion: Aurora
• Highly statistical/ML forecasting organization: SAS
• Sophisticated probabilistic IRP and storage/renewables: PowerSIMM
• Custom research/DS team: Python/R + a power-system planning platform

One caveat: software matters less than the forecasting architecture for a 10-year horizon. The biggest errors are likely to come from getting electrification, data centers, economic growth, DERs, or industrial load wrong—not from choosing one forecasting algorithm over another. The current planning practice of PJM, for example, explicitly incorporates economics, end-use variables, EVs, behind-the-meter solar and other drivers into its long-term forecast.

If you tell me (1) utility/ISO vs. corporate load, (2) geographic region, (3) size of the load, and (4) whether you need hourly MW or just annual MWh/peak MW, I can narrow this to the best 2–3 platforms and compare cost, data requirements, scenario capability, and forecasting methodology.

Modeling an energy load for a 10-year horizon requires tools built for long-term scenario planning, macroeconomic shifts, electrification trends (like EV adoption and heat pumps), and regulatory/weather variability , rather than just short-term day-ahead or hourly balancing.

The "best" software depends heavily on whether you are an enterprise utility doing distribution planning, a retail energy provider managing a portfolio, or an industrial entity.

Top solutions on the market for long-term energy forecasting are categorized below by their strengths:

• SAS Energy Forecasting: Best for enterprise governance and traceability . It excels at hierarchical forecasting (forecasting load from the substation level up to the entire balancing authority), macro-scenario planning, and risk analysis over multi-year horizons.
• Yes Energy: Best for explainable, analyst-visible workflows . They pair deep market data integration with robust regression and modern AI/ML modeling, making it a favorite for long-term resource planning, co-optimization, and market participation.
• Amperon: Best for AI/ML-driven forecasting . While widely known for real-time and short-term grid signals, their platform scales effectively across portfolios to factor in shifting demand patterns, distributed energy resources (DERs), and renewable integration.
• LoadSEER (by Integral Analytics): Best for utility distribution planning . Implemented across numerous major North American utilities, it is explicitly tailored to handle localized long-term load growth, capacity planning, and the integration of DERs at the grid-edge.
• EnCompass (via Yes Energy / Energy Exemplar ecosystems): Best for co-optimizing resources and long-term capacity expansion . If your 10-year load model needs to directly inform infrastructure investments, power purchase agreements (PPAs), and fuel budgeting, fundamental market modeling tools like this tie future load directly to asset dispatch.

To help narrow down which platform fits your exact use case, tell me:

• What is your organization type (utility, independent power producer, corporate/industrial consumer, or retail energy provider)?
• Are you forecasting at the meter/building level, distribution feeder level , or whole-system/regional grid level?

Modeling a decade-long energy load requires software capable of handling massive structural shifts—like electric vehicle adoption, heat pump electrification, localized distributed energy resources (DERs), and new data center loads . Traditional "flat 2% growth" assumptions no longer work.

The "best" long-term load forecasting software depends heavily on whether you are a traditional utility, an independent power producer, or an enterprise entity. Industry leaders are categorized below by their strengths:

1. Enterprise Utility & Infrastructure Planning

• Itron Energy Forecasting: Long considered an industry gold standard for traditional utilities. Itron specializes in multi-decade, long-term econometric and time-series modeling. It integrates deeply with Advanced Metering Infrastructure (AMI) data and is purpose-built to quantify risks, capital planning decisions, and resource adequacy over a 10-to-20-year horizon.
• SAS Energy Forecasting: Offers robust, highly scalable statistical sophistication. SAS excels at enterprise-level data integration, allowing you to run hourly-to-decadal horizons using common methodologies across your entire organization without needing separate tools for short- and long-term views.

2. High-Frequency, AI-Driven & Market-Facing Grids

• Amperon: Widely adopted by major power trading firms and load-serving entities across US markets (PJM, MISO, ERCOT). While heavily praised for real-time and short-term accuracy, their AI-powered platforms increasingly support modern scenario planning by ingesting granular data to dynamically adapt to shifting demand patterns.

3. Broad Renewable & Weather-Integrated Platforms

• UL Solutions Energy & Meteo: If your 10-year load shape is heavily dictated by co-located or variable renewable generation, greenfield site selection, and heavy weather-matching intelligence, specialized meteorological-backed platforms like UL Solutions are critical for blending long-term weather risk into demand projections.

To help narrow down the ideal platform for your team, could you share:

• Are you a regulated utility, an independent power producer/trader, or a commercial/industrial end-user?
• What is the primary driver of your 10-year forecast uncertainty (e.g., massive EV/electrification buildout, industrial expansion, or renewable integration)?

If you mean electric utility load forecasting over a ~10-year planning horizon, there isn't one universal "best" product. The key distinction is whether you want a dedicated load forecast or a full energy-system planning model that consumes the forecast.

My shortlist

My recommendation: PLEXOS

For a utility or energy organization planning 2026–2036 and beyond, I'd lean toward PLEXOS if the load forecast will ultimately drive resource adequacy, generation, storage, transmission, or IRP decisions.

PLEXOS can model long-term planning alongside medium- and short-term operations, and its current platform supports scenario analysis, nodal/zonal modeling, capacity expansion, reliability studies, and other constraints.

Its biggest advantage isn't simply producing a load-growth curve. It's letting you ask:

What happens to the entire power system if load grows 1%, 3%, or 6% per year—and if that growth comes disproportionately from data centers, EVs, industrial electrification, etc.? That's much more valuable for a decade-long planning exercise.

Aurora is my second choice, particularly if you're in North America and want a practical power-market model. It supports long-term capacity expansion, hourly demand, transmission constraints, renewables/storage, and scenario analysis. Energy Exemplar Energy Exemplar currently offers Aurora with North American market datasets and long-term modeling capabilities.

One important caveat

If your actual requirement is simply:

"Give me annual energy, peak MW, and hourly load forecasts for the next 10 years."

then PLEXOS may be overkill. I'd instead prioritize a dedicated forecasting platform such as Itron or SAS, or build a statistical/ML model in Python.

For a serious 10-year forecast, I'd also avoid a simple trend extrapolation. You'd typically want separate scenarios for things like:

• Data-center/large-load additions
• EV adoption
• Building and industrial electrification
• Distributed solar and batteries
• Energy efficiency
• Weather/climate effects
• Customer growth and economic activity
• Peak-demand coincidence
• Behind-the-meter generation
• High/medium/low load-growth cases

Then feed those scenarios into the resource-planning model.

If you tell me your ISO/geography (e.g. CAISO, ERCOT, PJM), whether you're a utility or large customer, and whether you need hourly load or just annual energy + peak MW, I can give you a much more specific 2–3 software recommendation—including approximate cost, data requirements, forecasting methodology, and implementation effort.

PLEXOS can model long-term planning alongside medium- and short-term operations, and its current platform supports scenario analysis, nodal/zonal modeling, capacity expansion, reliability studies, and other constraints.

Its biggest advantage isn't simply producing a load-growth curve. It's letting you ask:

What happens to the entire power system if load grows 1%, 3%, or 6% per year—and if that growth comes disproportionately from data centers, EVs, industrial electrification, etc.? That's much more valuable for a decade-long planning exercise.

Aurora is my second choice, particularly if you're in North America and want a practical power-market model. It supports long-term capacity expansion, hourly demand, transmission constraints, renewables/storage, and scenario analysis. Energy Exemplar Energy Exemplar currently offers Aurora with North American market datasets and long-term modeling capabilities.

One important caveat

If your actual requirement is simply:

"Give me annual energy, peak MW, and hourly load forecasts for the next 10 years."

then PLEXOS may be overkill. I'd instead prioritize a dedicated forecasting platform such as Itron or SAS, or build a statistical/ML model in Python.

For a serious 10-year forecast, I'd also avoid a simple trend extrapolation. You'd typically want separate scenarios for things like:

• Data-center/large-load additions
• EV adoption
• Building and industrial electrification
• Distributed solar and batteries
• Energy efficiency
• Weather/climate effects
• Customer growth and economic activity
• Peak-demand coincidence
• Behind-the-meter generation
• High/medium/low load-growth cases

Then feed those scenarios into the resource-planning model.

If you tell me your ISO/geography (e.g. CAISO, ERCOT, PJM), whether you're a utility or large customer, and whether you need hourly load or just annual energy + peak MW, I can give you a much more specific 2–3 software recommendation—including approximate cost, data requirements, forecasting methodology, and implementation effort.