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1 |
Model-free control method based on reinforcement learning for building cooling water systems: Validation by measured data-based simulation
روش کنترل بدون مدل مبتنی بر یادگیری تقویتی برای برای ساخت سیستم های آب خنک کننده : اعتبار سنجی با شبیه سازی مبتنی بر داده اندازه گیری شده-2020 In the domain of optimal control for building HVAC systems, the performance of model-based control
has been widely investigated and validated. However, the performance of model-based control highly depends
on an accurate system performance model and sufficient sensors, which are difficult to obtain for
certain buildings. To tackle this problem, a model-free optimal control method based on reinforcement
learning is proposed to control the building cooling water system. In the proposed method, the wet bulb
temperature and system cooling load are taken as the states, the frequencies of fans and pumps are the
actions, and the reward is the system COP (i.e., the comprehensive COP of chillers, cooling water pumps,
and cooling towers). The proposed method is based on Q-learning. Validated with the measured data
from a real central chilled water system, a three-month measured data-based simulation is conducted
under the supervision of four types of controllers: basic controller, local feedback controller, model-based
controller, and the proposed model-free controller. Compared with the basic controller, the model-free
controller can conserve 11% of the system energy in the first applied cooling season, which is greater than
that of the local feedback controller (7%) but less than that of the model-based controller (14%). Moreover,
the energy saving rate of the model-free controller could reach 12% in the second applied cooling
season, after which the energy saving rate gets stabilized. Although the energy conservation performance
of the model-free controller is inferior to that of the model-based controller, the model-free controller
requires less a priori knowledge and sensors, which makes it promising for application in buildings for
which the lack of accurate system performance models or sensors is an obstacle. Moreover, the results
suggest that for a central chilled water system with a designed peak cooling load close to 20 0 0 kW, three
months of learning during the cooling season is sufficient to develop a good model-free controller with
an acceptable performance. Keywords: Cooling water system | Cooling tower | Cooling water pump | Optimal control | Reinforcement learning | Model-free control |
مقاله انگلیسی |
2 |
Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings
یادگیری تقویتی عمیق برای بهینه سازی کنترل دمای داخلی و مصرف انرژی گرمایشی در ساخت-2020 In this work, Deep Reinforcement Learning (DRL) is implemented to control the supply water temperature
setpoint to terminal units of a heating system. The experiment was carried out for an office building
in an integrated simulation environment. A sensitivity analysis is carried out on relevant hyperparameters
to identify their optimal configuration. Moreover, two sets of input variables were considered for
assessing their impact on the adaptability capabilities of the DRL controller. In this context a static and
dynamic deployment of the DRL controller is performed. The trained control agent is tested for four different
scenarios to determine its adaptability to the variation of forcing variables such as weather conditions,
occupant presence patterns and different indoor temperature setpoint requirements. The
performance of the agent is evaluated against a reference controller that implements a combination of
rule-based and climatic-based logics. As a result, when the set of variables are adequately selected a heating
energy saving ranging between 5 and 12% is obtained with an enhanced indoor temperature control
with both static and dynamic deployment. Eventually the study proves that if the set of input variables
are not carefully selected a dynamic deployment is strictly required for obtaining good performance. Keywords: Deep reinforcement learning | Building adaptive control | Energy efficiency | Temperature control | HVAC |
مقاله انگلیسی |
3 |
Comfort evaluation of seasonally and daily used residential load insmart buildings for hottest areas via predictive mean vote method
ارزیابی راحتی ساختمانهای بار مسکونی فصلی و روزانه برای گرمترین مناطق با استفاده از روش پیش بینی میانگین رای گیری-2020 tIn this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mam-dani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonallyused appliances are considered for the analysis of the efficient energy management through these con-trollers. Energy management is performed using the two Demand Side Management (DSM) strategies:load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of thedaily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances,i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are appliedin this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of thehottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occu-pancy, price, decision control variables, priority and length of operation times of the appliances, whereasthe output parameters are: energy consumption, cost and thermal and appliance usage comfort. More-over, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger’spredictive mean vote method. The comfort is further investigated by incorporating the renewable energysources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllersas compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energyconsumption and cost of the proposed scenario. Keywords:Energy management | Thermal comfort | Appliance usage comfort | Fuzzy logic | Fuzzy inference systems |
مقاله انگلیسی |
4 |
Review on performance assessment of phase change materials in buildings for thermal management through passive approach
مروری بر ارزیابی عملکرد مواد تغییر فاز در ساختمانها برای مدیریت حرارتی از طریق رویکرد غیرفعال-2020 Latent heat energy storage (LHES) systems using phase change materials (PCMs) are well known for its
excellent thermal energy storage and release during melting and solidifications respectively. PCMs can
be efficiently deployed in applications where significant temperature difference exists in the system
for intermittent thermal energy storage. Several research contributions has been made on integrating
PCMs in buildings for thermal management, as it enhances building thermal inertia, reduces maximum
heat flux, shifts peak energy demand, reduces temperature fluctuations of air, etc., owing to its isothermal
behavior and high energy storage density during phase change. Results of several research articles reveal
that incorporation of PCM in buildings could significantly improve indoor comfort conditions and reduce
energy demand of Heating ventilation and air conditioning (HVAC) systems, provided appropriate PCM
selection, encapsulation methods, location deployed etc. This review paper is devoted to elucidate various
facts attributing PCM integration in buildings for thermal management through passive approach.
The facts includes performance of PCMs in buildings in terms of heat gain reduction, temperature attenuation,
peak energy demand shifting and energy saving potential, encapsulation deployed, are discussed
and presented in order to expedite the interpretation for future researchers, who took their research work
in the field of building thermal energy management. Keywords: Phase change material | Passive approach | Thermal energy management | PCM Encapsulation | Buildings |
مقاله انگلیسی |
5 |
Energy-Efficient Heating Control for Smart Buildings with Deep Reinforcement Learning
کنترل گرمای انرژی کارامد برای ساختمانهای هوشمند با یادگیری تقویتی عمیق-2020 Buildings account for roughly 40% of the total energy consumption in the world, out of which
heating, ventilation, and air conditioning are the major contributors. Traditional heating controllers
are inecient due to lack of adaptability to dynamic conditions such as changing user
preferences and outside temperature patterns. Therefore, it is necessary to design energy-ecient
controllers that can improvise occupant thermal comfort (deviation from setpoint temperature)
while reducing energy consumption. This research presents a Deep Reinforcement Learning
(DRL)-based heating controller to improve thermal comfort and minimize energy costs in smart
buildings. We perform extensive simulation experiments using real-world outside temperature
data. The results show that the DRL-based smart controller outperforms a traditional thermostat
controller by improving thermal comfort between 15% - 30% and reducing energy costs between
5% - 12% in the simulated environment. A second set of experiments is then performed for the
case of multiple buildings, each having its own heating equipment. The performance is compared
when the buildings are controlled centrally (using a single DRL-based controller) versus
decentralized control, where each heater is controlled independently and has its own DRL-based
controller. We observe that as the number of buildings and dierences in their setpoint temperatures
increase, decentralized control performs better than a centralized controller. The results
have practical implications for heating control, especially in areas with multiple buildings such
as residential complexes with multiple houses. Keywords : Deep reinforcement learning | Simulation | Occupant thermal comfort | Heating controller | HVAC |
مقاله انگلیسی |
6 |
Reinforcement learning for whole-building HVAC control and demand response
یادگیری تقویتی برای کنترل HVAC کل و پاسخ به تقاضا-2020 This paper proposes a novel reinforcement learning (RL) architecture for the efficient scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a commercial building while harnessing its de- mand response (DR) potentials. With advances in automated building management systems, this can be achieved seamlessly by a smart autonomous RL agent which takes the best action, for example, a change in HVAC temper- ature set point, necessary to change the electricity usage pattern of a building in response to demand response signals, and with minimal thermal comfort impact to customers. Previous research in this area has tackled only individual aspects of the problem using RL. Specifically, due to the challenges in implementing demand response with whole-building models, simpler analytical models which poorly capture reality have been used instead. And where whole-building models are applied, RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected. Thus, in this research, we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals. Our simulation results show that by applying reinforcement learning for normal HVAC operation, a maximum weekly energy reduction of up to 22% can be achieved compared to a handcrafted baseline controller. Furthermore, by employing a DR-aware RL controller during demand response periods, average power reductions or increases of up to 50% can be achieved on a weekly basis compared to the default RL controller, while keeping occupant thermal comfort levels within acceptable bounds. Keywords: Demand response | Reinforcement learning | Whole-building HVAC control | Distributed energy resources | Optimal HVAC energy scheduling |
مقاله انگلیسی |
7 |
Managing thermal comfort in contemporary high-rise residential buildings: Using smart thermostats and surveys to identify energy efficiency and comfort opportunities
راحتی مدیریت حرارتی در ساختمانهای مسکونی مرتفع امروزی: استفاده از ترموستات هوشمند و نظرسنجی برای شناسایی بهره وری انرژی و فرصت های راحتی-2020 Heating, ventilation and air conditioning (HVAC) operation is the largest contributor to residential building
energy use and can significantly influence occupant thermal comfort and behaviours. Despite the boom in highrise
residential building construction, little is known about the relationship between occupant comfort and behaviors
and HVAC operation in these contemporary buildings. In this study, connected thermostat data and
occupant surveys from 55 participants across two contemporary high-rise condominium buildings were used to
characterize this relationship to reveal opportunities for improved comfort and energy efficiency. Survey data
indicated that occupant thermal discomfort was prevalent across both buildings in the heating and cooling
seasons (48% and 53% reporting discomfort in heating and cooling seasons, respectively). The measured data
corroborated the survey findings showing that over-conditioning of suites is a chronic issue across seasons.
However, investigation into the temperatures of suites located in different parts of the buildings indicated that
the overheating was influenced by wind, solar radiation and winter stack effect, but not in the ways suggested by
building physics (e.g. air moving in opposite direction of predicted stack effect). This supports our hypothesis
that the MAU operational characteristics and incorrect balancing of air duct networks are likely causing overconditioning
but further investigation is ongoing to confirm this. Analysis of suite HVAC unit runtimes also
revealed low runtimes in heating season, which may limit the effectiveness of improved (e.g. occupant-based) insuite
HVAC controls, given the minimal space conditioning energy consumption in these particular suites. This
study demonstrates the potential for using connected thermostat data as a diagnostic tool to identify opportunities
for energy savings in the building. Keywords: Connected thermostats | Energy management | Residential building operation | Occupant comfort | HVAC | Commissioning |
مقاله انگلیسی |
8 |
Systematic approach to provide building occupants with feedback to reduce energy consumption
رویکرد سیستماتیک برای بازخورد دادن به سرنشینان ساختمان برای کاهش مصرف انرژی-2020 Many technical solutions have been developed to reduce buildings’ energy consumption, but limited
efforts have been made to adequately address the role or action of building occupants in this process. Our
earlier investigations have shown that occupants play a significant role in buildings’ energy consumption:
It was shown that savings of up to 20% could be achieved by modifying occupant behavior thorough
direct feedback and recommendations. Studying the role of occupants in building energy consumption
requires an understanding of the interrelationships between climatic conditions; building characteristics;
and building services and operation. This paper describes the development of a systematic procedure
to provide building occupants with direct feedback and recommendations to help them take
appropriate action to reduce building energy consumption. The procedure is geared toward developing a
Reference Building (RB) (an energy-efficient building) for a specific given building. The RB is then
compared against its given building to inform the occupants of the given building how they are using
end-use loads and how they can improve them. The RB is generated using a data-mining approach,
which involves clustering analysis and neural networks. The framework is based on clustering similar
buildings by effects unrelated to occupant behavior. The buildings are then grouped based on their
energy consumption, and those with lower consumption are combined to generate the RB. Performance
evaluation is determined by comparison of a given building with an RB. This comparison provides
feedback that can lead occupants to take appropriate measures (e.g., turning off unnecessary lights or
heating, ventilation, and air conditioning (HVAC), etc.) to improve building energy performance. More
accurate, scalable, and realistic results are achiveable through current methodology which is shown
through comparison with existing literature. Keywords: Energy use evaluation | Building energy management | Data mining | Occupant behavior |
مقاله انگلیسی |
9 |
A chance-constrained stochastic model predictive control for building integrated with renewable resources
کنترل پیش بینی یک مدل تصادفی محدود برای ساختمان یکپارچه با منابع تجدید پذیر-2020 Efficient operation of a building energy system integrated with renewable energy resources is one of the main
challenges associated with economic and flexible discussions. This work focuses on a chance-constrained stochastic
model predictive control (c-SMPC) based scheme to optimally schedule heating, ventilating and air
conditioning system (HVAC) and electric storage system (ESS) coordinately, to enable the highly efficient utilization
of solar power and economic energy conservation in the building. Specifically, adaptive control modes
provided for HVAC according to the time-varying occupancy status offer the building more energy flexibility
whilst maximally guarantee the inside thermal comfort with no physical constraint violation. In addition, the
uncertain factors, e.g., environment condition disturbances, are integrated into the optimization model by using
affine disturbance feedback and chance constraints formulation, providing the c-SMPC controller with tractability
and tunability in its temporal receding optimization process. The case of an office building integrated
with solar panels and ESS is studied to validate the proposed method, and results show that the method enables
an efficient and cost-effective mechanism of optimally coordinating the energy usage of the building. Compared
with the baseline controller, the proposed c-SMPC controller can achieve up to 46.6% energy cost reduction and
less comfort violation. Keywords: Model predictive control | Building energy management | HVAC system |
مقاله انگلیسی |
10 |
BIM-oriented data mining for thermal performance of prefabricated buildings
داده کاوی BIM گرا برای عملکرد حرارتی ساختمانهای پیش ساخته-2019 The use of energy efficiency procedures is a typical practice in building construction process that creates a huge
amount of data regarding the building. This is particularly valid in structures which include complex collaborations,
for example, ventilation, sunlight-based increases, inner additions, and warm mass. This paper proposes
a new approach for automating building construction when improving their energy efficiency, aiming to
foresee comfort levels based on Heating, Ventilating, Air Conditioning (HVAC), constructive systems performance,
environmental conditions, and occupant behavior. More specifically, it presents a research work about
thermal performance of prefabricated construction systems developed by an Argentine enterprise called Astori,
using two Knowledge Discovery in Databases (KDD) processes to extract knowledge. In this context, Building
Information Modeling (BIM) will give data to support the calculation to outline goal levels of a sustainable
building performance concerning classification systems. The data were collected from a project in Uruguay
referring to the construction systems and the energy efficiency of the building. The data mining tool SPMF was
used to test the performance of classification and its use in prediction. Particularly, FP-Growth Algorithm and
Clustering methodologies were used to analyze a combination of ambient conditions, in order to compare them
using Revit© software. The results generated by these methods can be generalized for a set of buildings, according
to the objective to be achieved concerning the thermal building performance Keywords: Data mining | Association rules | Clustering | Building information | Green buildings |
مقاله انگلیسی |