The Thermal Regime Simulation and the Heat Management of a Smart Building

Zakharov, Alexander; Zakharova, Irina G.; Romazanov, Artur; Широких, А.В.

doi:10.21684/2411-7978-2018-4-2-105-119

Cited by 8 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The state of the building regarding its thermal characteristics is determined by the corresponding parameters of its walls. To describe the model of a building it is important to take into account the layers of various materials for walls [14,15] as well as the other significant parts of the heat transfer process.…”

Section: A Thermal Characteristics Of a Buildingmentioning

confidence: 99%

See 1 more Smart Citation

Temperature Prediction in a Public Building Using Artificial Neural Network

Romazanov¹,

Захаров²,

Zakharova³

2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

View full text Add to dashboard Cite

The paper proposes an approach to predict the temperature in the rooms of a public building. The model of the building is described by the average temperatures in its rooms, the characteristics of external walls and heating elements. Weather conditions are determined by the temperature, speed and direction of the wind. The state of the thermal unit is described by the temperature of heat agent at the inlet and outlet of a heat supply system, as well as the flow rate. To build a predictive model, it is necessary to identify a nonlinear dependence of the temperature inside the room on these parameters. This problem is solved using a recurrent artificial neural network. The network based on gated recurrent unit was selected as the base for the network architecture in this approach. The features of this structure allow to take into account the sequence of data without using excessive parameters. To train the model and predict temperature values, measurement sequences of different lengths were used to determine the most effective model. The number of blocks corresponds to the length of the time series. The state of the network on the last block is a predicted temperature.

show abstract

Section: A Thermal Characteristics Of a Buildingmentioning

confidence: 99%

“…To establish the initial state of the ANN when training the model for the next room, you can use the values that were obtained when working with a room of the same class according to the classification of rooms by deviation from temperature norms [15,21,22].…”

Section: Ann Trainingmentioning

confidence: 99%

Temperature Prediction in a Public Building Using Artificial Neural Network

Romazanov¹,

Захаров²,

Zakharova³

2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

View full text Add to dashboard Cite

show abstract

“…In addition, the energy storage device is introduced into cold and heat power. The cogeneration system can achieve peak shaving and valley filling and further improve energy utilization [1]. Standard energy storage devices include thermal energy storage devices and batteries.…”

Section: Introductionmentioning

confidence: 99%

Thermal energy control in building energy system

Chen

2021

Therm sci

View full text Add to dashboard Cite

There is usually a waste of energy consumption in building systems. To help buildings reduce energy waste, the article established a building-sharing heat and power energy sharing system to achieve optimal energy allocation. Furthermore, the report determined the dual operation strategy model of using heat energy to determine power supply and electricity to determine heat energy. At the same time, we use stochastic programming and multi-objective optimization of the heating model and propose a two-level optimization model solution method based on the Benders decomposition algorithm. At the end of the thesis, the process was applied to actual cases to verify the method?s effectiveness.

show abstract