Kompiuterizuoto būsto šiluminio balanso modeliavimas naudojant MATLAB

Labai svarbu, kad šildymo sistema ne tik garantuotų komfortą, bet ir būtų ekonomiška. Šildymo sistemos ekonomiškumas priklauso ne tik nuo jos valdymo algoritmų, bet ir nuo radiatorių galingumo paskirstymo patalpose. Pasiūlytas pastato šiluminio balanso modelis, realizuotas naudojant MATLAB įrankį „Simulink“, leidžia analizuoti procesus šildymo sistemoje ir optimaliai paskirstyti šildymo elementų galingumą atskirose patalpose. Pateikiami modeliavimo rezultatai ir išvados.Smart House Heat Balance Modeling Using MATLABAntanas Mikuckas, Irena Mikuckienė, Egidijus Kazanavičius, Jonas Čeponis SummaryThe purpose of heating system is to create the best environment possible and to minimize energy consumption. Energy consumption in heating system depends not only on control algorithms of heating system, but also on power of heating units’ distribution. Heat balance model was developed using MATLAB. This model allows fi nding out optimal distribution of heating elements power. The results for residential house are shown. The heat consumption for a specifi ed time period was calculated.ight: 18px;">

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Pastato termofizinių savybių modeliavimas naudojant „Simulink“

Informacijos mokslai ◽

10.15388/im.2008.0.3432 ◽

2008 ◽

Vol 42 (43) ◽

pp. 121-127

Author(s):

Antanas Mikuckas ◽

Irena Mikuckienė ◽

Egidijus Kazanavičius ◽

Jonas Čeponis

Keyword(s):

Physical Properties ◽

Heating System ◽

External Temperature ◽

Thermal Transients ◽

Geometrical Characteristics ◽

Time Period ◽

Building Components ◽

Residential House ◽

Heating Energy Consumption ◽

Thermo Physical Properties

Šildant pastatus ne tik užtikrinamas komfortas, bet ir energetiškai teršiama aplinka, o gaminant kurą teršiama atmosfera. Šildymui mažiau sunaudojant energijos sutaupoma lėšų ir mažinama tarša. Pasiūlytas pastato šiluminio balanso modelis leidžia įvertinti įvairių veiksnių (atitvarų šiluminė varža, oro infiltracijos greitis, katilo galingumas, šildymo sistemos valdymo algoritmai ir t. t.) įtaką pastato šildymui sunaudojamos energijos kiekiui. Pateikiami modeliavimo rezultatai.Modeling thermo-physical properties of building using “Simulik”Antanas Mikuckas, Irena Mikuckienė, Egidijus Kazanavičius, Jonas Čeponis SummaryVarious models are used to study heat dynamics in buildings for evaluating heating energy consumption. This paper deals with model allowing to simulate thermal transients depending on the geometrical characteristics and thermo-physical properties of building components (exterior walls, internal partitions ceilings, floors and windows), external temperature variations and properties of heating system. The results for residential house are shown. The heat consumption for a specified time period was calculated. The heating energy conservation methods are analyzed and compared.

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Numerical modeling of the intelligent heating systems for living space

MATEC Web of Conferences ◽

10.1051/matecconf/201817809010 ◽

2018 ◽

Vol 178 ◽

pp. 09010

Author(s):

Victorita Radulescu

Keyword(s):

Numerical Modeling ◽

Heating System ◽

Hot Water ◽

Atmospheric Conditions ◽

Heating Systems ◽

Living Space ◽

Time Period ◽

Residential House ◽

Conventional Systems ◽

House Structure

Nowadays, the intelligent solution of heating and hot water supply in residential house has become a current domain in the domain of energy efficiency analysis. It is presented the mathematical model and some numerical simulations for intelligent heating systems, ventilation, and air conditioning into a building structured on two floors, with spaces with intermittent occupancy, between certain hours. The heating system was structured into a permanent correlation with the reference temperature and the hot water consumption, corresponding to the time-period spent by the inhabitants in the living rooms. The control algorithm is a combination of fuzzy systems, anticipation systems and conventional systems. The numerical modeling analyzes both periods, the inhabited and the holidays. As input data were considered the atmospheric conditions and solar radiation, house structure, as to obtain the output data, scheduled solution of heating the building. A solution of ceiling, estimation of windows behavior, internal and external walls is presented. The air temperature is regulated via a three-way valve commanded by an actuator, whose time constant is adjustable. The experimental validation of the obtained results was tested in a building system from the metropolitan area of the city.

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HEAT DEMAND FOR HEATING APARTMENT BUILDINGS/GYVENAMŲJŲ NAMŲ FAKTINIO ŠILUMINĖS ENERGIJOS POREIKIO SULYGINIMAS SU PROJEKTINE ŠILDYMO SISTEMŲ GALIA

Journal of Civil Engineering and Management ◽

10.3846/13921525.2000.10531615 ◽

2000 ◽

Vol 6 (5) ◽

pp. 366-370

Author(s):

Jūratė Karbauskaitė ◽

Vytautas Stankevičius

Keyword(s):

Energy Consumption ◽

District Heating ◽

Heating System ◽

Heat Consumption ◽

District Heating System ◽

Apartment Buildings ◽

Heat Demand ◽

Heated Area ◽

Design Values ◽

Design Value

In this paper the results of statistic analysis of heat consumption in apartment heating systems for Lithuania are discussed. Kaunas district heating system data are used for the analysis. Total sum of buildings involved is about 1900, including 1550 with the average heated area of 4000 m2. It has been established that real heat consumption in apartment buildings is less than the design heat demand (Fig 1), especially in small buildings (Fig 2). The distribution of monthly differences is presented in Fig 3. The difference during months does not depend on average outdoor temperature, but it could be caused by temperature fluctuations and solar radiation. It is quite important to determine the reasons of different heat consumption in buildings. For this purpose 20 dwelling houses of various design and building period, with various energy consumption problems have been selected for more detailed energy audit. Volumes of external building elements, changes in destination of premises, heated area have been estimated as well as the state of heat supply sub-station equipment. According to the data obtained, the energy consumption was determined for standard month at mean indoor and outdoor climate values. The results are compared with real energy consumption in the selected buildings and design values. It has been established that the inadequacies in exceeded energy consumption over design values are mostly caused by incorrect heated area registration and premises destination change, in a less range by absence of maintenance, eg broken outside doors, damaged roofs etc. Energy consumption in dwelling houses with design indoor temperature and normal maintenance level usually is near to the design value or less up to 10%. In dwelling houses, in which energy consumption is defined as being of less design value, some energy saving measures are applied, eg temperature in spaces is lowered up to 16°C, about half of balconies are glassed, electric stoves for cooking are installed as additional heat source. Such apartment buildings, as a rule, do not have premises of other destination. By such means near 40% of heat is saved.

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Electric Bus Indoor Heat Balance in Cold Weather

Applied Sciences ◽

10.3390/app112411761 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11761

Author(s):

Gabriel Chiriac ◽

Dumitru Dorin Lucache ◽

Costică Nițucă ◽

Alin Dragomir ◽

Seeram Ramakrishna

Keyword(s):

Energy Consumption ◽

Heat Loss ◽

Heat Balance ◽

Calculated Data ◽

Heating System ◽

Electricity Production ◽

Cold Weather ◽

Pollution Level ◽

Heat Transfer Theory ◽

Electric Bus

The use of electric buses is increasing all over the world; this is due to the aim of limiting pollution in heavily urbanized areas. Using electric buses is one element of the desire to drop local pollution to zero emissions. The necessary electricity can be generated through centralized production, and in the case of electric buses, the pollution level is directly proportional to the amount of electricity produced. Their limited onboard power needs optimization, both in terms of traction and in auxiliary energy consumption. Heating in electric buses consumes the most energy from the auxiliaries, which can reduce the range of the vehicle up to a half, or more in the coldest days of the winter months. In this context, a precise estimation of heat loss and of the energy necessary for heating electric buses is crucial. Using the heat transfer theory, the heat balance method, and the U-value estimation, this article estimates the heat loss for a typical 12 m electric bus for a harsh winter day. Thermal simulations were made in order to estimate the heat flux through the structure of the bus (windows, walls, roof, and floor). Heat loss components were calculated in order to determine the most affected zones of the bus. The calculated data for the energy necessary to heat the bus were compared with the heating system data from an electric bus. By optimizing the necessary auxiliary energy consumption, the emissions at the source of electricity production will be significantly reduced.

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Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

Energies ◽

10.3390/en14040997 ◽

2021 ◽

Vol 14 (4) ◽

pp. 997

Author(s):

Davide Coraci ◽

Silvio Brandi ◽

Marco Savino Piscitelli ◽

Alfonso Capozzoli

Keyword(s):

Energy Consumption ◽

Temperature Control ◽

Weather Conditions ◽

Heating System ◽

Building Envelope ◽

Indoor Temperature ◽

Model Free ◽

Occupancy Patterns ◽

Online Implementation ◽

Indoor Temperature Control

Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperature to radiant terminal units of a heating system serving an office building. The controller was trained online, and a preliminary sensitivity analysis on hyperparameters was performed to assess their influence on the agent performance. The DRL agent with the best performance was compared to a rule-based controller assumed as a baseline during a three-month heating season. The DRL controller outperformed the baseline after two weeks of deployment, with an overall performance improvement related to control of indoor temperature conditions. Moreover, the adaptability of the DRL agent was tested for various control scenarios, simulating changes of external weather conditions, indoor temperature setpoint, building envelope features and occupancy patterns. The agent dynamically deployed, despite a slight increase in energy consumption, led to an improvement of indoor temperature control, reducing the cumulative sum of temperature violations on average for all scenarios by 75% and 48% compared to the baseline and statically deployed agent respectively.

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