Sunshine Duration Recorders TRIZ Cases

See how verified seasonal energy loads and meteorological data enable accurate photovoltaic fle

See how distributed sky cameras and 3D cloud reconstruction predict solar irradiance occlusion,

See how expressing energy load as a function of outdoor temperature enables remote HVAC auditin

See how segmented neural networks reduce retraining time when pyranometers change, providing ma

See how expressing energy load as a function of outdoor temperature in point-intercept form ena

See how distributed digital cameras generate 3D sky models to predict solar irradiance from clo

See how solar intensity sensors detect approaching clouds to preemptively adjust photovoltaic p

See how qualitative weather descriptions are converted to quantitative coefficients and combine

See how digital analysis of utility load and weather data enables remote building energy audits

Qualitative weather reports are converted into irradiance inputs and combined with satellite imagery to improve local solar estimates without dense sensors.

3D aerial data, GIS, and shadow simulation assess rooftop solar potential accurately without repeated site visits, cutting evaluation time and cost.

Machine learning combines PV generation, weather, and terrain data to estimate solar radiation more accurately under cloud and mountain effects.

Combining sky-image analysis with weather data improves solar radiation and PV output prediction for better plant planning and operation.

By combining sunshine simulation, PV loss modeling, and discounted cash flows, this case improves solar power plant valuation under weather and market uncertainty.

Measured PV output and solar data are used to infer tilt, azimuth, shading, and inverter parameters for faster, more accurate fleet forecasting.

Mileage rewards for transmitting sunlight, temperature, location, and time data help increase vehicle solar energy participation.

Empirical weighting of irradiance and clear-sky data estimates normalized irradiation for more reliable photovoltaic fleet energy forecasts.

Equalizing lens portions spread light from multiple emitters to widen rain sensing range while preserving solar radiation detection accuracy.

Clear-sky output and local cloud cover data are combined to forecast photovoltaic generation more accurately for grid production scaling.

Historical output and solar data are used to infer PV tilt, azimuth, shading, and inverter parameters for more accurate energy forecasting.

Aerial panel mapping plus meteorological data estimates behind-the-meter solar output to guide grid upgrades and reduce blackout risk.

Bellwether meters in solar clusters enable real-time generation forecasts without third-party weather services, cutting latency and meter deployment.

A leaf wetness and temperature model predicts disease severity quickly from hourly weather data, reducing computation for targeted treatment.

A trained model estimates surface irradiance from 2D aerial images, avoiding costly 3D data for fast solar potential mapping.

Deep learning estimates surface irradiance from 2D aerial images, avoiding costly 3D data for faster solar planning.

A 360° dual-camera setup captures sky and ground data together to determine irradiance components and cloud velocity with fewer sensors.

Voltage time-series from solar homes are cross-correlated to track cloud motion and improve near-term utility demand forecasting.

Segmented photo-detecting cells and 3D imaging capture reflected light by direction and spectrum for accurate bifacial irradiance data and yield optimization.

Grid-based light sensors map sunlight across a location, helping adjust irrigation and select grass varieties without manual assessment.

Uniform heliostat control causes unnecessary operations when DNI varies across the field; cloud-shadow mapping enables localized control.

Predefined sky areas around the sun position avoid tracking each cloud, reducing calculation cost for sunshine prediction.

This case uses infrared, GPS, and air-pressure data to build cloud maps while reducing camera complexity and computational burden.

LSTM and DNN models forecast local weather so electrochromic windows can adjust tint proactively for energy savings and comfort.

A computer system infers photovoltaic operational specifications using net load data and solar resource measurements.

An embedded sky monitoring device uses patch-based luminance detection to resolve high-dynamic range coverage limits in standard sensors.

A compact sun sensor uses a single photodiode and computation module to generate position signals.

Computer processor method estimates energy losses due to shading by integrating measured and modeled data inputs.

A device estimates solar flux using temperature bias between shaded and exposed sensors.

An infrared cloud detector measures sky temperature to control tintable windows, resolving low light detection errors.

Module-level power electronics derive latitude and longitude from sunrise and sunset data to validate as-built configurations against design specifications.

Information processing device calculates sunny and shady place indices to determine photosynthetic photon flux density across a measurement target region.

Black and silver plates thermally isolated from the casing measure solar radiation heat accurately without increasing device weight.

Empirical exponential functions correlate point-to-point sky clearness, reducing data collection infrastructure costs while maintaining estimation accuracy.

A 3D cloud map and 2D irradiance projection forecast solar fluctuations to adjust inverter output.

A pyranometer control unit calculates average solar radiation values using selectable averaging functions.

A computer system calculates a cloud velocity field from sky images to predict short-term solar irradiance.

Dual sensors measure parallax to calculate cloud height, compensating for shadow effects and improving material classification accuracy.

Infrared cloud detector measures sky temperature to control tintable windows despite low light conditions.

Computing device calculates solar irradiance across multiple geographical points using weather data and atmospheric attenuation models.

Integrated light shielding plates create an insertion space for the circuit board, eliminating separate protective cases and reducing installation time.

A Solar Access Measurement Device generates 2D matrices from 3D CAD models to determine solar radiation exposure.

Ground-based cloud shadow sensors detect irradiance changes to adjust photovoltaic plant output via inverters.

Infrared cloud detector measures sky temperature via thermal radiation to determine cloud conditions using ambient temperature differences.

System calculates normalized irradiation through clearness indexes to forecast fleet power output without dense sensor networks.