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Related Concept Videos

Specific Heat01:16

Specific Heat

67.4K
The specific heat capacity of a substance refers to the energy required to increase the temperature of one gram of that substance by one degree Celcius. Specific heat capacity is often represented in calories (cal), grams (g), and degrees Celsius (oC), but can also be expressed in joules (J), kilograms (kg), and Kelvin (K), among other units.
For example, increasing the temperature of one gram of water by 1°C requires one calorie of heat energy and can be written as 1 cal/g-°C, or...
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Quantifying Heat02:46

Quantifying Heat

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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...
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Heat Flow and Specific Heat01:12

Heat Flow and Specific Heat

6.8K
Heat is a type of energy transfer that is caused by a temperature difference, and it can change the temperature of an object. Since heat is a form of energy, its SI unit is the joule (J). Another common unit of energy often used for heat is the calorie (cal), which is defined as the energy needed to change the temperature of 1 g of water by 1 °C, specifically between 14.5 °C and 15.5 °C, since the energy needed shows a slight temperature dependence. Another commonly used unit is...
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Heating and Cooling Curves02:44

Heating and Cooling Curves

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When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
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Leveling Effect01:29

Leveling Effect

1.4K
In acid-base chemistry, the leveling effect refers to the limitation imposed by the solvent on the strength of acids and bases in solution. When a base stronger than the solvent's conjugate base is used, it deprotonates the solvent until the base is entirely consumed, making it ineffective against weaker acids. Conversely, an acid stronger than the solvent's conjugate acid protonates the solvent until the acid is depleted, rendering it ineffective against weaker bases. Essentially, the...
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Heat Engines01:10

Heat Engines

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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...
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High-resolution Patterning Using Two Modes of Electrohydrodynamic Jet: Drop on Demand and Near-field Electrospinning
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Enabling Demand Side Management: Heat Demand Forecasting at City Level.

Petri Hietaharju1, Mika Ruusunen2, Kauko Leiviskä3

  • 1Control Engineering, Environmental and Chemical Engineering, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland. petri.hietaharju@oulu.fi.

Materials (Basel, Switzerland)
|January 13, 2019
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Summary

Accurate heat demand forecasting in buildings is key for energy efficiency. New models predict city-wide heat needs 48 hours in advance with 4% error, enabling better demand-side management.

Keywords:
NARXbuildingdemand responsedistrict heatingheat demandparameter estimationprediction

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Area of Science:

  • Energy Systems Engineering
  • Building Science
  • Computational Modeling

Background:

  • Implementing energy efficiency measures in heating and building sectors is crucial.
  • Demand-side management (DSM) can optimize city-level heat demand by engaging individual buildings.

Purpose of the Study:

  • To develop and validate models for forecasting heat demand from individual buildings to a city-wide scale.
  • To assess the feasibility of applying these models for demand-side management and predictive optimization.

Main Methods:

  • Applied two distinct models to forecast heat demand.
  • Utilized district heating data from over 4000 buildings for model validation.
  • Compared forecast simulations with measured data.

Main Results:

  • Achieved an average relative error of 4% for city-level heat demand forecasts 48 hours ahead during the heating season.
  • Model accuracy in individual buildings varied depending on building type and heat demand patterns.
  • Demonstrated that forecasting accuracy, limited measurement data, and quick calibration times allow application to entire building stocks.

Conclusions:

  • The developed models are suitable for city-wide heat demand forecasting and enabling demand-side management.
  • Predictive optimization of heat demand at the city level can be achieved, leading to enhanced energy efficiency.
  • The approach facilitates the integration of individual buildings into broader energy management strategies.