Leading-Edge AI Demand Forecasting: Bottom-Up, Top-Down & DERs
Cloud-based AI Electricity Load Forecasting Software Solutions
A complete suite of high-precision AI forecasting solutions delivering total, net-load, and DER demand intelligence from 5-minute intraday operations to 25+ year planning across utilities, energy retailers, grid operators and power market participants
Top-Down Demand Forecasting
Medium and long-term grid forecasting at circuit, substation and transmission levels. Powered by macroeconomic modeling, climate trends, and hierarchical disaggregation for IRP and CapEx planning.
Bottom-up Demand Forecasting
Hyper-local Medium and long-term spatial load forecasting built from customer AMI meter profiles, zip codes, and rate classes. Reconciled at the feeder boundary to meet strict regulatory planning mandates.
Intraday to 14 DA Utility Load
High-resolution 1-14 day operational forecasts integrating SCADA and AMI interval data. Optimizes unit commitment, wholesale procurement, and feeder peak management.
Intraday to 14 DA Retail Load Forecasting
Translates monthly meter reads, customer tariffs, and standard load profiles into high-precision probabilistic net-load shapes. Designed for energy trading desks to minimize BRP imbalance penalties and maximize margin.
Peak-Load Forecasting
1 - 7 DA Coincident Peak Probabilistic scoring & graduated alerting service targeted for each RTO or power market.
Medium- to Long-term Top-down Spatial Grid Demand Forecasting AI Solutions
Tailored for Global Grid & Distribution Network Operators and Utilities
Long-term grid planning can no longer rely on linear extrapolation in an era of rapid electrification, energy-intensive industrial expansion (e.g., data centers), and distributed energy growth. QR ALPS (AI Load Prediction System) Top-down demand forecasting evaluates broad macroeconomic indicators, policy targets, and long-term climate projections to establish an auditable system-wide baseline for bulk transmission and distribution networks.
Key Applications
- CapEx & system infrastructure budgeting: Generates 1 to 20+ year peak and energy forecasts to justify bulk capital allocation and de-risk major substation/transmission expansion projects.
- Macroeconomic & policy driver modeling: Incorporates regional GDP growth, population trends, industrial interconnection pipelines, and state decarbonization mandates into econometric AI baselines.
- Streamlined regulatory & rate case compliance: Delivers defensible, fully auditable multi-decade load shapes required for Integrated Resource Plans (IRPs), Public Utility Commission (PUC) rate filings, and regional ISO transmission adequacy studies.
Platform Deliverables
- Econometric hierarchical allocation: Models top-level macro system trends first for statistical stability, then mathematically disaggregates growth down to regional substations and load pockets.
- Multi-Scale temporal resolution: Projects multi-decade demand at hourly resolution, with 5, 15, or 30-minute intervals derived via historical diurnal and seasonal load profiles.
- Synthetic climate & weather projections: Generates multi-year diurnal weather profiles and seasonal Heating/Cooling Degree Hour (HDH/CDH) trends to simulate long-term climate shift scenarios.
- Substation SCADA normalization: Automatically adjusts historical time-series data for inter-substation load transfers to ensure AI models train on clean baseline data.
- Macro DER & long-term climate scenario modeling: Applies econometric decomposition to system-level net demand to estimate baseline unmetered rooftop PV, effective BESS capacities, and weather sensitivity (HDH/CDH). Planners can run multi-decade stress tests to simulate the grid-wide impact of accelerated EV, heat pump, and distributed solar adoption on 20-year transmission baselines.
Medium- to Long-term Bottom-up Hyper-Local Circuit & Feeder AI Load Forecasting Software
Designed for Worldwide Distribution Utilities
To meet evolving regulatory requirements such as NYISO and ISO-NE 20-year circuit-level forecasting mandates utilities must understand load growth at the grid edge. QuantRisk’s Bottom-Up AI Forecaster models demand where power is consumed: starting from smart meters (AMI), consumer rate classes, zip codes, and localized DER assets, then aggregating upward through network topology.
Key Applications
- Feeder & asset capacity planning: Pinpoints exact distribution feeders and transformers hitting thermal limits over 5, 10, or 20-year horizons to optimize capital expenditure and deploy Non-Wires Alternatives (NWAs).
- Hyper-Local DER & electrification tracking: Models neighborhood-level adoption trajectories for Electric Vehicles (EVs), Heat Pumps, Behind-the-Meter (BTM) Solar PV, and BESS based on local demographics, tariffs, and EV registries.
- ISO/RTO grid operators spatial compliance: Satisfies strict regulatory rules requiring multi-decade, circuit-level spatial load projections for formal Distribution System Planning (DSP).
Platform Deliverables
- AMI load segmentation: AI load forecasts modeling at various consumer segments by filtering historical smart meter interval data by consumer type, tariff classes, zip codes, and spatial land-use data, or EV registry.
- Feeder SCADA reconciliation & data cleansing: Uses real-time feeder SCADA readings as operational ground truth to calibrate, cleanse, and reconcile raw, noisy AMI consumer aggregations at the circuit boundary.
- Topology-Aware event normalization: Automatically filters and normalizes historical circuit SCADA data for switching events, tie-switch closures, and feeder load reassignments.
- Topological roll-up: Sums cleansed feeder forecasts upward through GIS connectivity maps to substations and bulk grid connection points, ensuring full mathematical alignment across all voltage levels.
- Circuit-level DER & AMI disaggregation: Uses spatial decomposition algorithms to isolate unmetered rooftop PV, home battery storage, and localized EV charging profiles directly from raw smart meter (AMI) net-load feeds. Enables distribution planners to map neighborhood-level adoption trends and superimpose scenario models onto individual feeder boundaries.
Intraday to 14 DA Short-term Distribution Utility AI Load Forecasting Solutions
Utilities & Load Serving Entities
Bridge real-time physical grid operations with short-term wholesale power procurement. QuantRisk’s Intraday to 14-DA Forecaster converts high-frequency SCADA and AMI meter data into actionable operational insights, helping utilities optimize energy sourcing, prevent asset thermal overloads, and mitigate market risk in DER-dense grid environments.
Key Applications
- Optimized energy sourcing & dispatch: Schedule thermal generation, dispatch Battery Energy Storage Systems (BESS), and optimize short-term Power Purchase Agreements (PPAs) using high-accuracy 1- to 14-day load projections.
- Portfolio risk & imbalance management: Mitigate market risk exposure and protect against volatile wholesale imbalance penalties across real-time and day-ahead markets.
- Distributed energy (PV & EV) impact quantification: Isolate and evaluate the real-time grid impact of behind-the-meter solar generation, battery activity, and concentrated EV charging.
- Feeder & substation congestion prediction: Anticipate circuit-level line loading, transformer stress, and reverse power flows 1 to 14 days in advance to maintain voltage stability.
- Transformer overload & asset reliability: Perform thermal stress analysis on critical distribution transformers to schedule proactive maintenance and prevent catastrophic equipment failure.
- Operational network planning & DER integration: Evaluate short-term hosting capacity constraints to dynamically manage local flexibility services and DER dispatch.
Platform Deliverables
- AMI load & consumer segmentation: Constructs segment-specific AI load models by filtering historical smart meter interval feeds by customer type, rate/tariff structures, zip codes, spatial land-use data, and regional EV registries.
- Multi-Horizon forecast coverage: Delivers intraday, day-ahead (DA), and 14-day-ahead AI demand forecasts at 5, 15, 30, or 60-minute resolutions for both total net customer load and behind-the-meter DER injections and withdrawals.
- Uncertainty management: AI probabilistic forecasts performed at desired confidence levels such as low (P5), medium (P50) and high (P95) quantify demand and weather uncertainty.
- Demand decomposition & thermal stress testing: Deconstructs high-frequency SCADA & AMI net-load feeds to quantify real-time solar irradiance impacts, unmetered PV injections, and battery activity. Allows operational teams to anticipate 5-minute reverse power flows, transformer thermal loading, and short-term feeder congestion.
Short-term, Intraday to 7 DA, Retail Net-Load Forecasting Services
One-stop Demand Platform for Energy Retail Providers
Translate complex customer meter data, tariffs, and unmetered DER activity into actionable trading intelligence. QR Load Forecaster empowers energy suppliers to eliminate cash-flow volatility, optimize wholesale market bidding, and defend profit margins against costly imbalance penalties across dynamic retail portfolios.
Key Applications & Benefits
- Wholesale energy sourcing & bidding: Optimize Day-Ahead (DAM) and Real-Time (RTM) market bids, manage bilateral contracts, schedule callable options, and execute targeted hedging strategies.
- Imbalance volumetric risk management: Minimize Balancing Responsible Party (BRP) cash-out penalties by modeling portfolio volumetric risk across variable weather conditions.
- Portfolio & customer segment analytics: Unlock deep portfolio insights by evaluating load profiles across high-impact customer segments, tariffs, and regional utility zones.
- DER flexibility & adoption assessment: Evaluate the exact portfolio impact of customer behind-the-meter (BTM) PV generation, battery storage, and EV charging behavior.
- Revenue protection & theft detection: Automatically analyze interval consumption patterns to identify abnormal load drops, metering anomalies, or energy theft.
Platform Deliverables & System Features
- Multi-Horizon forecast coverage: Delivers intraday, day-ahead (DA), and 14-day-ahead AI demand projections at 5, 15, 30, or 60-minute settlement resolutions for both customer net-load and BTM DERs.
- Dynamic portfolio segmentation: Models load dynamically across all retail-relevant customer tiers, including tariff classes, rate structures, customer types, and regional utility service territories.
- Probabilistic AI uncertainty management: Generates probabilistic forecast distributions across user-defined confidence intervals (P5, P10, P50, P90, P95) to help operational teams quantify weather and load uncertainty.
- Automated QR Data Hub integration: Streamlines multi-utility operations by seamlessly ingesting Electronic Data Interchange (EDI) billing feeds, third-party Utility Data Platforms, and disparate metering formats into a clean, unified data stream.
- DER, load disaggregation & scenarios: the platform uses mathematical demand decomposition algorithms to extract unmetered rooftop PV, BESS, and EV charging profiles from raw net-load data using solar irradiance, effective capacities, and Heating/Cooling Degree Hours (HDH/CDH). The calibrated load model is superimposed on load forecasts to perform what-if-scenarios analysis and stress testing.
Peak Load Forecasting
Utilities, Retail Energy Providers, Large Industrial & Commercial Consumers
Industry-Standard Coincident Peak (CP) Forecasting & Automated Alerting
System-wide peak demand charges can represent over 30% of a facility’s or retailer’s annual capacity and transmission bill. QuantRisk delivers a targeted, 1-7 day probabilistic peak predictions engineered specifically to mitigate ISO/RTO capacity tags and transmission tariffs.
- Probabilistic scoring & graduated alerts: The model runs algorithms hourly with peak load forecast probability P10, P50, P90 that automatically step up operational alerts (Watch → Alert → Critical) as peak grid conditions develop, preventing curtailment fatigue.
- Configurable ISO/RTO market operator billing rule engines: the model allows easy configuration of regulatory billing formulas for major grid operators, including ERCOT 4CP, PJM 5CP (PLC Tags), and NYISO/ISO-NE/MISO 1CP (ICAP obligations)m as well as European markets.
- Automated DER & control system integration: The solution connects via REST APIs or SCADA protocols to automatically trigger BESS battery discharges, facility EMS load shedding, or onsite backup generation during high-risk peak windows.
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Global / Multi-Regional Average Demand Forecast Accuracy Analysis, October 21, 2026
- Pre-Trading Execution: In the German power market, official DA demand forecasts are released after market close at 10:00 AM; QR ALPS executes its predictive run earlier at 8:00 AM daily, mirroring real-time utility workflows.
- Telemetry Volatility: High-frequency 5-minute smart meter feeds utilized by utilities and grid operators often contain artificial noise and high-frequency jaggedness, which inherently introduces baseline variance into the reported MAPE values below.
| Accuracy Metric | Day-Ahead | 7-Day Ahead |
|---|---|---|
| MAPE | 2.4 % | 3.1 % |
Universal, Market-Agnostic AI Demand Forecasting Solutions
QR ALPS (AI Load Prediction System) is a Universal, Market-agnostic AI Demand Platform
This solution is engineered to operate seamlessly across any global power market, regardless of regional market rules, settlement intervals, or grid topologies. Designed to support transmission operators, distribution utilities and load serving entities, and energy retail providers worldwide, the platform adapts instantly to local grid structures, regulatory frameworks, and regional telemetry data.
Featured Dashboard: CAISO Regional & TAC Demand Forecasting
QR ALPS key AI load modeling features, dashboards, reports, and multi-market adaptability are showcased using CAISO regional demand or TAC data as a representative use case.
CAISO TAC Probabilistic Demand Forecasting & Visual Analytics
QR ALPS (AI Load Prediction System) advanced AI architecture generates hourly probabilistic load forecasts across CAISO Transmission Access Charge (TAC) areas, at customizable confidence levels (P5, P50, P95) to explicitly quantify demand risk for grid operators and market participants. The platform automatically aggregates segment-level probabilistic forecast bands into regional net load shapes, displaying predictions directly alongside published actual demands. Built-in KPI engines calculate continuous accuracy metrics, including Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), while interactive time-series charts provide dynamic zooming, range-scrolling, and granular inspection capabilities.
CAISO TAC AI Load Forecasting & Structural Component Decomposition
QR ALPS (AI Load Prediction System) proprietary mathematical load model decomposes observable CAISO net demand into five core structural components: temperature-independent baseload, cooling-driven demand (CDH), heating-driven demand (HDH), EV charging, and unmetered behind-the-meter (BTM) solar PV generation. Calibrated using hybrid statistical and AI algorithms on recent seasonal data, these structural parameters are superimposed onto baseline AI forecasts. This framework allows grid planners and analysts to apply structural shocks to individual demand drivers for advanced scenario modeling and stress testing. Interactive dashboards visually stack these underlying vectors to display gross versus net load profiles while offering full drill-down capabilities into localized Transmission Access Charge (TAC) areas.
Full load forecast dashboards can be viewed
QR ALPS Advanced Demand Modeling and AI Forecasting Technology
QR ALPS provides a rigorous framework for demand AI forecasting & modeling by decomposing demand into its primary structural drivers: base load, cooling and heating degree components, and behind-the-meter (BTM) PV capacity, while incorporating seasonality and temporal effects.
Why QuantRisk Outperforms the Competition
- Multi-Horizon & multi-Resolution flexibility: The AI engine can be configured to seamlessly span operational real-time intraday, 14-day trading horizons, and 1- to 25+ year long-term planning models. Matches any ISO/RTO settlement or planning window with 5, 15, 30 and 60 minutes interval resolutions.
- True probabilistic forecast & risk quantification: Eliminates single-point forecast failure by generating full probability distributions (P5, P10, P50, P90, P95). Gives traders and planners exact confidence bounds to manage tail risk, avoid volumetric imbalance charges, and optimize reserves. Read about our methodology
- Advanced load decomposition & scenario engine: Uses complex mathematical load modeling to isolate net demand into core baseload, weather-sensitive, behind-the-meter (BTM) PV, and EV charging components. Enables planners to stress-test extreme climate shifts and rapid electrification trajectories via a specialized scenario toolbox.
- Read more about our BTM PV estimation methodology. Read about our methodology
- Read more bout our Cooling and Heating Degree modeling methodology. Read about our methodology
- Automated No-Code AI pipeline: Reduces AI model deployment from months to days using a fully automated AutoML pipeline. Handles data ingestion, automated feature engineering, model selection, scenario analysis, and execution without requiring dedicated data science teams or custom coding. Read more
- Enterprise-Grade infrastructure & flexible deployment: QR ALPS (AI Load Prediction System) is a production-ready cloud instance engineered for ultra-fast, customer-specific model tuning. Offers two versatile delivery options:
- Forecast-as-a-Service (FaaS): Instant, ready-to-use data streams delivered via Web Dashboards and REST APIs with zero implementation friction.
- Software-as-a-Service (SaaS): A dedicated, private-cloud environment to configure, train, backtest, and deploy fully custom demand forecast models.
- Seamless API & dashboard integration: Serves high-precision forecasts directly into downstream ETRM, EMS, or SCADA systems via interactive dashboards and automated REST APIs supporting JSON and CSV formats. Read more
- Cost-effective pricing. Whether deployed via Forecast-as-a-Service (FaaS) or Software-as-a-Service (SaaS), QR ALPS is billed under a single, cost-effective annual subscription. Your flat-rate fee covers unlimited total and concurrent users with zero per-seat charges, full software licensing, routine maintenance, major annual platform upgrades, and continuous technical support under an all-time warranty.cor
Forecast Resolution & Frequency
QR ALPS engine supports forecasting demand across multiple temporal resolutions.
Temporal resolution. QR AI forecasts load at its native temporal resolution, typically from 5 to 60-minute intervals. The platform can also generate forecasts at aggregated corser resolutions, e.g., from 5-minute load data it can generate 15, 30 and 60 minutes forecasts.
Going in reverse direction is also possible. E.g, 5-min meter data can exhibit artificial volatility caused by meter reading limitations and communication jitter, rather than true underlying consumption behavior. The AI engine can roll up the high-frequency 5-min data to a lower-noise 30-min resolution, then train and forecast the AI model and apply a method to bring the forecasts back to the original 5-min scale.
Medium-term 12-month load forecasts can be performed at daily resolution and scaled to hourly with some profiling methods.
Long-term (1–20 years) load forecasts are performed at monthly resolution and scaled to hourly with some profiling methods.
Forecasts frequency & updated revisions
- RTD or real-time forecasts are executed every 5, 15 or 60 minutes.
- DA up to 14 DA forecasts are typically executed between 7 to 10 am.
Contextual Feature Engineering
QR ALPS enables a range of contextualization features. Unlike purely autoregressive features, contextualization enables AI models to anticipate behavioral shifts without recent signals, e.g., after holidays or regime changes.
For more details see https://quantrisk.com/electricity-forecasting-solutions-software-service-features.
Time-aware features capture seasonal, weekly and intraday recurring patterns & dynamics: hour of day, day of week, weekend/weekday patterns, holidays and special days.
Calendar and behavioral features are derived from jurisdiction-specific holiday calendars embedded in AI models frameworks to account for significant demand and price differences exhibited during public holidays. The method maps mid-week holiday to the nearest weekend profile both in AI training and in forecasting.
Peer-point contextual features. Lagged indicators enable the AI model to capture autocorrelation temporal dependencies and recurring embedded behavioral patterns. Previous period values, same-hour previous day values, same-hour previous week values.
Data Feature Engineering
QR ALPS enables fully visual data engineering: users can drag and drop advanced mathematical transformations directly onto predictor time series, eliminating the need for coding.
For more details see https://quantrisk.com/electricity-forecasting-solutions-software-service-features.
Gap-filling methods interpolate missing data to improve & stabilize AI model performance.
Outlier detection and replacement methods eliminate the effect of exceptional behaviors not to be learned or reproduced.
Additional processors can be applied when they can improve an AI model: percent change, scaling, quantization, encoding, normalization, smoothing and noise reduction.
The parameters of each method can be configured and edited on screen in web GUIs.
Demand Data Reconciliation
Utilities face inconsistencies between bottom-up demand estimates derived from aggregated smart meter data and top-down demand estimates derived from normalized SCADA measurements.
These discrepancies arise from technical and non-technical losses, inaccuracies in SCADA network normalization, and errors introduced during smart meter data backfilling.
To resolve these inconsistencies, our platform implements a constrained optimization–based reconciliation framework that jointly adjusts SCADA normalization factors and meter-level backfilling parameters to minimize the residual gap between top-down and bottom-up demand estimates. The optimization is subject to physical, statistical, and operational constraints to preserve network feasibility and temporal consistency.
This reconciliation process produces a single, internally consistent demand signal across the network hierarchy, materially improving the accuracy of both system-level (top-down) and customer-level (bottom-up) demand forecasts, and enabling more accurate load allocation across substations, feeders, and customer classes.
Dynamic Customer Segmentation
The AI engine provides advanced, no-code customer segmentation algorithms:
- Attribute-based filtering such as customer class (residential, commercial, industrial), tariff group, contract type, geographic or feeder-level identifiers.
- Unsupervised machine-learning clustering such as K-Means and variants, Gaussian Mixture Models, with the use of engineered features (daily, weekly, seasonal profiles, hour-of-day averages, load ramps peak-to-off-peak ratios) automatically cluster consumers with similar load behavior. This can also support detection of behind-the-meter PV, BESS, EV-charging clusters.
Using constrained optimization, the reconstruction process distributes monthly energy volumes across intraday time slices that preserve energy balance, temporal consistency, and segment-specific behavioral characteristics.
Meter Data Backfilling
Consumer AMI smart meter data (5, 15, 30-min, hourly) can present gaps or corrupted readings due to variety of reasons, delayed uploads, communication failures, device malfunctions, or data validation rejections, meter commissioning or replacement, adhesion of new consumers.
Meter data backfilling is the mathematical process of reconstructing missing meter data, to produce a complete, consistent time series suitable for forecasting, analytics and settlement. Depending on data availability and the duration of the gap, we offer different backfilling methods:
- Use the same meter’s historical profiles (e.g., same hour, day type, season) to estimate missing values.
- Proxy or peer-based estimation based on similar meters, customer segments, or feeder-level profiles scaled to the target meter. Apply pattern or machine learning.
- Adjust reconstructed meter data so that aggregated consumption aligns with known totals, e.g., monthly billing records.
Net Load, Weather & PV Modeling
Consumers’ AMI meter readings record load net of behind-the-meter PV generation. PV quantity is not directly observable and must be deduced from a mathematical model of net load. To answer these issues
We use industry standard mathematical Net-Load Models decomposing net load into base load, cooling degree hourly (CDH), heating degree hourly (HDH) and PV component based on and Global Horizontal Irradiance GHI.
Net-Load model parameter estimation. Constrained least-squares optimization methods are used to estimate the model parameters corresponding to base load, CDH, HDH and PV based on historic load, temperature GHI data observations.
All estimated model parameters are time-dependent across a 24-hour cycle, capture intraday load elasticity and diurnal behavioral shifts, and mid-day PV penetration effects, industrial versus residential sensitivity differences, and the accuracy of what-if weather and PV scenarios.
AI Load Model w. Weather & PV Scenarios
Weather induced scenarios. The Net-Load Model is used to define weather induced load impact scenario. E.g., (+5°) is converted into a 24-hour temperature profile adjustment, preserving diurnal structure. The model then computes: Cooling Degree Hours (CDH) and Heating Degree Hours (HDH), and impact on demand while preserving intraday elasticity, nonlinear weather sensitivity, peak-load amplification effects.
PV Scenarios. The net-load model is used to define PV-impact scenarios on net load.
Cloudy & clear sky scenarios can be created by specifying a Given GHI level e.g., 750 W/m2. The model transforms the 750 GHI level into a daily GHI profile across sunlight hours and applies that to the PV components of the load model.
PV capacity change scenarios, modeling unavailability or increased PV adoptions, are defined by modifying the capacity coefficient in the Net-Load model.
AI Load Shift Scenario Modeling
Modifying external feature data, when performing long-term demand forecast. E.g., creating average or extreme growth or decay scenarios on macroeconomic indicators, unemployment rate, CPI, 3M Yield, GDP, population growth, etc. Econometrics data as well as their long-term forecasts are provided.
Modifying demand continuously can be defined by applying a math processor to model continuous growth rates, e.g., +3% annual growth or a -5% decay in some geolocation or some consumer type.
Modifying demand by step function shift can be applied as time dependent patterns on top of AI demand forecast in the future. For example, a 4 MW demand increase will occur due to a new mall opening in 2 years. A step function pattern processor can be defined to increase demand forecasts by 4 MW from 9 am to 10 pm while the mall operates, starting only 2 years from now.
Synthetic Consumer Load Reconstruction
Retail energy providers and utilities with limited smart meter coverage, have no access to usable fine granularity meter.
We offer algorithms to overcome this challenge by reconstructing synthetic demand profiles at 5 to 60 min resolution, at the finest possible customer segmentation, using historical monthly meter consumption from billing, load profiles libraries (weekday, weekend, seasonal), and estimated PV injections.
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We offer a new paradigm: state-of-the-art AI demand forecasting solution delivered as a web service, pay-as-you-go, no implementation or IT integration required
Our business model eliminates costly implementation, system integration, long deployment cycles, and license overhead, while delivering full flexibility and scalability. You pay a one-time configuration fee for Stage 1. Thereafter, you can choose between a yearly license or a pay-per-execution model for Stage 3, aligning cost directly with usage and value. We recommend to start with a free trial and validate the results before committing.
The platform offers ready cloud instances loaded with premade templates and automated pipelines for data ingestion & management, data processing, feature engineering, and AI load forecasting models, integrated dashboards and reports.
These can be hosted as a private instance on our cloud, on your cloud or on-premise, ensuring full flexibility and compliance.
Our expert team leverages QR AI Demand Cloud automated pipelines to configure AI demand models tailored to your requirements, including short or long-term forecasting, and bottom-up (meter/feeder-level) or top-down (SCADA system-level) approaches.
The customized forecasting model is deployed according to the Stage 0 options.
QR AI Demand Datahub offers tools to upload demand data via CSV or API. The platform automatically validates, structures, and organizes incoming data into unified demand data models, fully optimized for AI consumption. Weather & irradiance data are automatically fetched based on demand geolocation.
The demand data hub can be deployed on our cloud or your preferred infrastructure, ensuring full flexibility and compliance.
Stage 2 can operate in batch mode for long-term forecasting or in continuous mode for intraday and day-ahead (DA) demand predictions.
Forecast outputs are delivered in interactive web dashboards and can be exported via API for seamless integration into your preferred data platforms.
See QuantRisk Cutting-edge No-Code Auto-ML AI Technology in Action
QuantRisk’s no-code web dashboard enables visual, on-screen configuration of probabilistic AI models with custom confidence percentiles. Users can drag and drop features and mathematical processors directly into the workspace, interactively assembling the AI model in real-time.
On-screen drag-and-drop probabilistic AI load forecast modeling with custom confidence levels.
On-screen feature selection using multi-algorithm correlation analysis to identify optimal forecasting features.
The QR DataHub automates data ingestion across global power markets without SQL coding. Users can visually map data sources, configure transformations, and generate database tables on the fly. External drivers such as multivariable weather feeds, calendar flags, and lag metrics are added via simple drag-and-drop actions, allowing complex AI models to be built within minutes.
Visual drag-and-drop Feature Engineering pipeline from external features insertion such as weather, to contextual time and calendar, and peer-point features.
QR DataHub centralizing and managing raw load data and price feeds across global power markets: data fetching, mapping, and insertion.
QR DataHub captures segment-level load attributes such as commercial, residential, baseload, and solar PV. The AI engine enables seamless pre- and post-forecast aggregation. Users can combine segments pre-forecasting using visual formula builders to train a single target model, or forecast each driver independently and aggregate outputs post-forecasting with custom growth weighting.
Bottom-up load forecasting model: aggregating load segments prior to model training.
Bottom-up load forecasting model: forecasting individual load drivers independently and aggregating outputs post-forecast.
QR Dashboards and Reporting modules can be provisioned in hours to deliver customized views of data feeds, AI models, and accuracy analytics. The web interface streamlines operational workflows, corporate tracking, and regulatory compliance reporting.
Live, interactive load forecast dashboard with real-time operational telemetry, reports and access to AI models.
Comprehensive load visualization dashboard featuring accuracy metrics (MAPE/MAE), reports and manual data export.
FAQ
Is your solution fully AI-based, or is AI planned for a future release?
QR ALPS (AI Load Prediction System) is 100% AI-native today. It features a no-code AI architecture that automates the entire load forecasting pipeline, allowing users to visually build, tune, and execute models via an intuitive drag-and-drop workflow without writing code.
Do we need a dedicated data science team to operate the platform?
What does your onboarding process and implementation timeline look like?
We follow an Agile implementation methodology. Because QR ALPS is a ready-to-use cloud engine with pre-built data ingestion, AI modeling, and dashboard frameworks, you receive a dedicated cloud instance right away. Our team configures the system to your specific grid or retail operations with minimal IT overhead, working directly with your key operational stakeholders. The system is modular, so you can begin using live forecast outputs immediately while custom reports and additional modules are finalized. Implementation is handled entirely in-house by QuantRisk engineers without third-party consultants.
Where and how is the platform hosted and deployed?
We deploy QR ALPS directly into a dedicated private cloud environment of your choosing (such as AWS, Azure, or Google Cloud). You retain full control and ownership of the underlying cloud infrastructure costs. The platform includes a built-in enterprise DataHub that securely centralizes all raw telemetry, meter data, and output forecasts. Because the dedicated instance and database can be provisioned in any geographic cloud region, you can easily comply with local data sovereignty, privacy, and regulatory requirements.
How is the software licensed?
What happens if we need custom reports or new forecast configurations later?
Is a free trial available?
What is included in your customer support program?
How do you manage platform maintenance and bug fixes?
How are software upgrades handled?
What is your platform uptime, and do you offer a Service Level Agreement (SLA)?
Yes, we provide a formal, binding SLA protocol with severity-based response times. Our platform is engineered for enterprise reliability, maintaining 99.7%+ operational uptime. Total aggregated planned maintenance across the entire year requires approximately 24 hours (1 day), ensuring your forecasting operations remain online and dependable around the clock.
What data sources and telemetry formats are required to train and run the load forecast models?
QuantRisk ALPS ingests standard SCADA feeds, AMI smart meter interval data, regional weather forecasts/historical observations (including solar irradiance and HDH/CDH), customer tariffs, and EDI billing feeds. Our built-in DataHub handles automated data cleansing, missing value imputation, and topology reconciliation. Data can be in csv or Json format. We can configure connectivity to third-party APIs within an hour of configuration.
How do you measure model accuracy, and can we benchmark against our current forecasting baseline?
The platform includes built-in accuracy visualizers and automated backtesting frameworks. Performance is continuously tracked using standard industry metrics, including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Weighted Absolute Percentage Error (WAPE), across all horizons. You can benchmark live forecasts or historical backtests directly against your existing internal baselines during the free trial.
Is QuantRisk QR ALPS compatible with specific US RTO/ISO and international market structures and settlement intervals?
Yes. QuantRisk ALPS supports power market structures across North America (PJM, CAISO, ERCOT, NYISO, ISO-NE, MISO, SPP), European, Asia Pacific and other international grid operators. The engine handles 5, 15 and 60 minute settlement intervals for both day-ahead (DAM) and real-time (RTM) market workflows.
