Market Predictions TRIZ Cases

See how adaptive blast freezing schedules use simulation models to balance food preservation re

See how scheduling oil return and defrosting operations based on power demand requests aligns H

See how cascaded feedback control uses pre-cooling and thermal mass to reduce HVAC energy costs

See how predictive scheduling of oil return and defrosting operations based on power demand and

See how predictive control optimizes grid power purchase and battery storage timing to reduce H

See how cascaded feedback control uses real-time energy pricing and setpoint adjustment to redu

Feedback-based HVAC control uses energy setpoints, pricing data, and building conditions to cut peak demand without losing temperature comfort.

Feedback control and pre-cooling help HVAC systems cut peak-period power use while maintaining building temperature during demand limiting.

Evaluates battery combinations and charge schedules to cut grid power and replacement costs in energy storage operation.

A single telematics marketplace shares vehicle operator profiles across parties while tiered access controls primary and secondary inferences.

Reinforcement-learning trading agents forecast grid supply and demand, price electricity, and keep customer preferences visible in peer-to-peer trading.

Day-ahead energy pricing and vehicle telemetry are used to schedule EV charging in lower-cost windows that cut peak grid load.

A shared telematics marketplace uses GPS and motion sensors to build operator profiles, reducing multi-device data collection complexity.

Predicted power prices guide in-travel SOC adjustment, lowering charging cost anxiety while avoiding heavy control-unit computation.

Driving-condition-based battery selection uses vehicle status, energy prices, and user habits to cut cost while balancing range and fuel economy.

An e-cloud pools energy equivalent quantities from suppliers and allocates credits and debits to cut peak demand and consumption charges.

Sensor data from autonomous steering, acceleration, and parking improves vehicle risk assessment and enables more accurate insurance pricing.

Allocates plant load by generator efficiency and rated-load fit, then bids only when total requested power can be supplied.

Dynamic AI allocation lets autonomous EV fleets switch between transport and grid services using emissions, pricing, and battery-health data.

Compares driver behavior and autonomous feature risk at disengagement to keep the safer controller in charge and improve insurance assessment.

Forecasted price peaks and distributed open-loop HVAC control cut aggregator costs while preserving home privacy and comfort.

Usage-based time-zone pricing helps EV charging stations cut energy waste and improve utilization with discounts and surcharges.

EV eco-data is used to generate, adjust, and trade carbon credits with flexible minimum-unit rules for more accurate allocation.

A server adjusts EV charging, discharging, and time-based rates to maximize V2G profit while keeping the vehicle at the required SoC.

A flexibility index quantifies each DER's contribution, enabling fairer profit allocation and lower computation in energy-sharing aggregators.

Periodic output allocation balances adjustment cost, output change rate, and remaining capacity to suppress frequency fluctuations.

A unified telematics marketplace uses one SDK and tiered inferences to share operator profiles across parties without exposing proprietary models.

A shared telematics marketplace uses one sensing setup to serve multiple parties while updating inferences and model evaluations.

Compares pre- and post-event driver inferences in a telematics marketplace to evaluate management changes without multiple party-specific devices.

Price-forecasted control shifts electrolyzer output between hydrogen production and ancillary services to cut power cost and capture grid revenue.

Sensor-based risk scoring compares driver behavior with autonomous feature risk to prevent unsafe handover and support more accurate insurance pricing.

Real-time demand, user pricing, and storage state are combined to prioritize urgent loads and improve energy storage economic value.

Forecasted grid guidance uses renewable output, demand, curtailment, and emissions data to shift electricity use toward cleaner hours.

Real-time grid, source, and price forecasts create energy windows that shift usage toward cleaner, lower-cost electricity.

AI models route processing requests to edge nodes using the best available energy mix, cutting delay and energy cost under fluctuating supply.

Coordinated ML agents balance energy arbitrage, charging, backup power, and battery degradation in dynamic BESS operation.

Forecast-based charging uses cost, greenness, driver behavior, and vehicle usage data to schedule lower-cost, lower-emission EV charging.

A separate energy-harvesting antenna keeps a capacitor charged during metal shielding, enabling faster communication recovery and event detection.

A mobile-network assigning authority schedules EV charging from route, load, timing, and electricity price data to cut costs without losing readiness.

Equipment-level power data is used to forecast facility demand and keep total usage across multiple facilities within set limits.

Adaptive multi-faceted approximation converts infinite-dimensional power dispatching into a faster, more accurate finite-dimensional model.

When sale price meets or exceeds purchase price, the server authorizes EV battery power export to improve community EMS convenience.

Predicted EV charging demand sets a dynamic facility power limit that avoids peak exceedance while preserving charging-service profit.

A low-voltage three-phase motor and control module balance high air speed with lower noise and stable power use in portable fans.

Real-time EV charging recommendations use vehicle status and user preferences to plan stops, reduce range anxiety, and simplify charging decisions.

Predicted price and demand data guide battery charging before peak-rate periods, then discharge power to the building when electricity costs rise.

Spike detection on differenced load signals finds outage gaps, then localized loadshape forecasts fill them to improve utility demand forecasting.

Movement and use history are converted into part degradation and saleable price estimates, helping buyers find used vehicle parts faster.

A unified telematics marketplace shares operator data profiles across services while enforcing classification-based access and privacy controls.

A controller balances direct travel and ad impact by choosing shuttle routes with priming-matched points of interest near drop-offs.

Market-driven bidirectional charging shifts EV energy transfer by price and grid conditions to cut costs, reduce waste, and ease volatility.

Sensor and vehicle feature data refine autonomous vehicle risk scoring, enabling usage-based premiums tied to route, time, weather, and location.

Onboard sensors detect medical emergencies, route the vehicle to care, and feed autonomous feature performance into more accurate insurance pricing.

Auto-generated in-vehicle surveys capture driver opinions on service stops, building better provider lists for future navigation choices.

Cloud-based SoC and power maps predict regional EV charging demand and station supply to improve scheduling and availability.

A controller compares POIs on alternate shuttle routes to raise advertising exposure for contracted businesses while limiting extra travel time.

A split reward and penalty scheme helps energy storage scheduling models converge faster and more precisely under price, action, and state changes.

Blockchain-recorded harmony scores and PSO-based pricing improve transparent peak-valley matching between grids and building energy users.

Real-time telematics and demand signals tailor ride-share insurance coverage and pricing, improving peak-time protection without full-time cost.

Shared latent representations and adversarial learning infer protected groups to debias ML models without demographic data.

Seasonal and theme affinity scores from transaction history help tailor interface elements to changing user preferences in e-commerce.

Multi-dimensional purchase embeddings replace simple word matching to improve recommendation relevance using buying history and regional patterns.