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Related Concept Videos

Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
Energy and Power Signals01:17

Energy and Power Signals

In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
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Conservation of Energy: Application01:12

Conservation of Energy: Application

When solving problems using the energy conservation law, the object (system) to be studied should first be identified. Often, in applications of energy conservation, we study more than one body at the same time. Second, identify all forces acting on the object and determine whether each force doing work is conservative. If a non-conservative force (e.g., friction) is doing work, then mechanical energy is not conserved. The system must then be analyzed with non-conservative work. Third, for...
Conservation of Mechanical Energy01:05

Conservation of Mechanical Energy

The mechanical energy E of a system is the sum of its potential energy U and the kinetic energy K of the objects within it. What happens to this mechanical energy when only conservative forces cause energy transfers within the system—that is, when frictional and drag forces do not act on the objects in the system? Also assume that the system is isolated from its environment; in other words no external force from an object outside the system causes energy changes inside the system.
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Related Experiment Videos

EcoImpact: energy conservation using data-driven model predictive control and interpretable machine learning in the

Aseel Hussien1, Aref Maksoud2, Shouib Nouh Ma'bdeh2,3

  • 1University of Sharjah, Sharjah, 27272, United Arab Emirates. ahussien@sharjah.ac.ae.

Scientific Reports
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

EcoImpact integrates machine learning and explainable AI for building energy management. This framework enhances control performance and provides transparent decision support for energy optimization.

Keywords:
Data-Driven Model Predictive ControlEnergy ForecastInterpretable Machine LearningMachine LearningSHapley Additive ExplanationsSum of Squared Errors

Related Experiment Videos

Area of Science:

  • Building energy management
  • Artificial intelligence in energy systems
  • Predictive control

Background:

  • Conventional predictive control prioritizes prediction accuracy over transparency.
  • Building energy optimization requires interpretable models for effective decision support.

Purpose of the Study:

  • To present EcoImpact, a novel interpretable predictive-control framework for building energy management.
  • To integrate data-driven forecasting, iterative control optimization, and explainable artificial intelligence.
  • To enhance control performance and provide transparent decision support.

Main Methods:

  • Utilized Random Forest and XGBoost models within a custom predictive-control structure.
  • Employed SHAP (SHapley Additive exPlanations) for model interpretability.
  • Integrated machine learning-based control with explainable AI in a unified workflow.

Main Results:

  • Demonstrated substantial error reduction across iterative runs for both Random Forest (final error: 109.19) and XGBoost (final error: 92.53).
  • SHAP analysis identified key operational and environmental factors influencing energy predictions.
  • Validated the effectiveness of the EcoImpact framework in improving predictive performance.

Conclusions:

  • EcoImpact successfully combines predictive control and explainable machine learning for transparent building energy management.
  • The framework enhances predictive performance while ensuring model behavior is interpretable.
  • Supports more efficient, reliable, and explainable building energy-management strategies.