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Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Precipitation Processes01:12

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Energy and Power Signals

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Related Experiment Videos

From Forecast to Action: A Deep Learning Model for Predicting Power Outages During Tropical Cyclones.

Yongchuan Yang1, Naiyu Wang1, Zhenguo Wang2

  • 1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Accurate tropical cyclone (TC) outage forecasting is improved with the SpatioTemporal Outage ForeCAST (STO-CAST) model. This deep learning framework provides updated, high-resolution predictions during storm events, enhancing power system resilience.

Keywords:
emergency decision supportpower outage predictionreal‐time data assimilationspatiotemporal predictiontropical cyclones

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Meteorology

Background:

  • Tropical cyclones (TCs) cause significant power outages, impacting communities and electric power systems.
  • Existing outage prediction models lack the ability to update forecasts dynamically as storm conditions evolve.
  • Accurate, high-resolution outage forecasting is crucial for effective emergency response and mitigation planning.

Purpose of the Study:

  • To introduce the SpatioTemporal Outage ForeCAST (STO-CAST) model, a novel deep learning framework for dynamic, high-resolution power outage forecasting during TCs.
  • To enable real-time, observation-updated rolling inference for outage prediction throughout TC events.
  • To enhance the resilience of electric power systems against intensifying TC threats.

Main Methods:

  • Developed a spatiotemporal deep learning framework (STO-CAST) integrating static infrastructure data with dynamic meteorological and outage sequences.
  • Implemented state-dependent, observation-updated rolling inference for continuous forecast refinement.
  • Produced hourly outage forecasts at a 4 km resolution with dual-horizon capabilities (6-hour nowcasting and 60-hour long-term forecasting).

Main Results:

  • The STO-CAST model demonstrated operational value in a case study of Typhoon Muifa (2022) using a Leave-One-Storm-Out framework.
  • The model successfully tracked evolving outage hotspots and provided diagnostic insights into forecast errors.
  • Error decomposition distinguished contributions from model limitations, meteorological uncertainty, and observation gaps.

Conclusions:

  • STO-CAST offers a scalable and interpretable framework for improving power outage forecasting during TCs.
  • The model enhances situational awareness for real-time response and informs proactive planning for resource staging.
  • The framework supports risk-informed emergency management and bolsters power system resilience against extreme weather events.