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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Updated: Nov 30, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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Probabilistic Load Forecasting for Building Energy Models.

Eva Lucas Segarra1, Germán Ramos Ruiz1, Carlos Fernández Bandera1

  • 1School of Architecture, University of Navarra, 31009 Pamplona, Spain.

Sensors (Basel, Switzerland)
|November 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new probabilistic load forecasting method for intelligent buildings, using a white-box building energy model (BEM) to account for weather forecast uncertainties. The approach accurately predicts energy load variations, improving smart grid management.

Keywords:
building energy modelsmonitoringprobabilistic load forecastingreliabilityuncertainty analysisweather forecastwhite-box models

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Area of Science:

  • Building energy systems
  • Smart grid technology
  • Data-driven forecasting

Background:

  • Accurate load forecasting is crucial for intelligent buildings and smart grids.
  • Traditional methods struggle with input uncertainties like weather forecasts.
  • Existing probabilistic methods often use black-box models, limiting component analysis.

Purpose of the Study:

  • To develop a probabilistic load forecasting methodology that incorporates weather forecast uncertainty.
  • To utilize a white-box building energy model (BEM) for detailed analysis.
  • To fill the gap in literature regarding component-specific performance evaluation in probabilistic forecasting.

Main Methods:

  • Developed a probabilistic load forecasting approach using a white-box Building Energy Model (BEM) in EnergyPlus.
  • Employed Gaussian kernel density estimation (KDE) to convert point forecasts into probabilistic forecasts.
  • Generated hourly uncertainty maps showing prediction intervals and error probabilities based on monitoring data.

Main Results:

  • The methodology successfully generated hourly uncertainty maps for load forecasting.
  • Prediction intervals consistently covered actual values above the 80% confidence level in a real-world case study.
  • The approach proved effective even with limited historical data for uncertainty mapping.

Conclusions:

  • The proposed white-box model methodology provides accurate probabilistic load forecasts by accounting for weather data uncertainty.
  • This approach enhances the reliability of load forecasting for intelligent buildings and smart grids.
  • The method allows for performance evaluation of specific building components within the forecast.