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Related Concept Videos

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 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...
Heat Engines01:10

Heat Engines

A heat engine is a device used to extract heat from a source and then convert it into mechanical work used for various applications. For example, a steam engine on an old-style train can produce the work needed for driving the train.
Whenever we consider heat engines (and associated devices such as refrigerators and heat pumps), we do not use the standard sign convention for heat and work. For convenience, we assume that the symbols Qh, Qc, and W represent only the amounts of heat transferred...
Energy to Drive Translocation01:37

Energy to Drive Translocation

Mitochondrial protein import is powered by two distinct energy sources: ATP hydrolysis and electrochemical potential across the inner membrane. Newly synthesized precursors are bound by cytosolic chaperones of the Hsp70 family, which guide them to the import receptors on the mitochondrial surface. Utilizing the energy of ATP hydrolysis, Hsp70 chaperones transfer these precursors to the TOM receptors on the mitochondrial outer membrane.
Generally, polypeptides are unfolded by two distinct...
Power and Energy01:12

Power and Energy

The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
Carnot Cycle and Efficiency01:26

Carnot Cycle and Efficiency

The Second Law of Thermodynamics asserts that it's impossible for any heat engine to achieve 100% efficiency. While contemplating the maximum possible efficiency, Nicolas Sadi Carnot conceptualized an ideal heat engine. This engine gets its energy from a high-temperature reservoir. It then performs some work and releases the remaining energy into a low-temperature reservoir.The Carnot cycle, named after Sadi Carnot, is fully reversible. The cycle consists of four distinct stages. In the first...

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Related Experiment Video

Updated: Jul 13, 2026

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs
10:36

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs

Published on: November 3, 2023

AI-driven optimization: revolutionizing energy efficiency in modern buildings.

Hamoud H Alshammari1

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia. hhalshammari@ju.edu.sa.

Scientific Reports
|July 11, 2026
PubMed
Summary

NODE-RL-BEM, a new continuous-time approach, enhances building energy management by integrating neural ordinary differential equations and reinforcement learning. This intelligent system achieves significant energy savings and maintains occupant comfort, optimizing building operations.

Keywords:
Adaptive HVAC controlContinuous-time reinforcement learningIntelligent building energy managementMulti-objective energy optimizationNeural ordinary differential equations

Related Experiment Videos

Last Updated: Jul 13, 2026

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs
10:36

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs

Published on: November 3, 2023

Area of Science:

  • Building energy management
  • Artificial intelligence in smart buildings
  • Control theory and optimization

Background:

  • Growing global energy demand and decarbonization goals necessitate advanced building energy management systems.
  • Conventional control strategies face challenges with temporal continuity, adaptability, and performance in diverse building environments.
  • Existing methods struggle with nonlinear dynamics and multi-objective operational trade-offs.

Purpose of the Study:

  • To introduce NODE-RL-BEM, a unified continuous-time optimization paradigm for intelligent building energy management.
  • To jointly model building system dynamics and learn adaptive control policies using integrated data.
  • To address limitations of conventional discrete-time and simulation-dependent control strategies.

Main Methods:

  • Developed NODE-RL-BEM, integrating heterogeneous operational data, temporal state embeddings, and neural ordinary differential equation modeling.
  • Employed multi-objective reinforcement learning within a cohesive architecture for predictive and responsive energy optimization.
  • Utilized continuous-time dynamics learning for improved predictive fidelity and smooth state evolution.

Main Results:

  • Achieved 42-48% energy savings and maintained comfort violations below 0.5%.
  • Improved indoor air quality by 28-35% across diverse datasets.
  • Demonstrated strong transferability and stability with a generalization score of 0.91.

Conclusions:

  • NODE-RL-BEM offers a novel continuous-time dynamic-policy learning paradigm for sustainable and autonomous building energy management.
  • The framework effectively integrates predictive modeling with real-time adaptive control for dynamic environments.
  • Scalable applicability to multi-zone buildings confirms practical deployment feasibility.