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1 |
Using reinforcement learning for maximizing residential self-consumption - Results from a field test
استفاده از یادگیری تقویتی برای به حداکثر رساندن خود مصرفی مسکونی - نتایج یک آزمون میدانی-2020 This paper presents the results from a real residential field test in which one of the objectives was to maximize the instantaneous self-consumption of the local photovoltaic production. The field test was part of the REnnovates project and was conducted in different phases on houses in several residential districts located in Soesterberg, Heerhugowaard, Woerden and Soest, the Netherlands. To maximize self- consumption, buffered heat pump installations for domestic hot water and stationary residential battery systems were chosen due to their respective thermal and electrical storage capacities. The algorithm used to tackle the associated sequential decision-making problem was model-based reinforcement learning. The proposed algorithm learns the stochastic occupant behavior, uses predictions of local photovoltaic production and considers the dynamics of the system. The results show that this algorithm increased the average self-consumption percentage of the local PV generation (used instantaneously in situ ) on average by 14%, even if only buffered heat pump installations for domestic hot water were used. This increase was achieved without causing any perceived discomfort to the residential end users. The average energy shifted per day from the solar production period to the night by the 2 kW/3.6 kWh batteries was 1.5 kWh. The main contribution of this work was therefore the real field implementation of the proposed algorithm. The results demonstrate that it is possible to improve even further the integration of local production using flexible loads. Keywords: Reinforcement learning | Q-Learning | Field test | Solar PV generation | Thermal storage | Thermostatically controlled loads | Electrical storage | Battery | Residential loads |
مقاله انگلیسی |
2 |
The reinforcement learning method for occupant behavior in building control: a review
روش یادگیری تقویتی برای رفتار ساکنین در کنترل ساختمان: یک بررسی-2020 Occupant behavior in buildings has been considered the major source of uncertainty for
assessing energy consumption and building performance. Modeling frameworks are usually
built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to
apply that experience to a similar but distinct environment. For complex and dynamic
environments, the development of smart devices and computing power makes intelligent
control methods for occupant behaviors more viable. It is expected that they will make a
substantial contribution to reducing global energy consumption. Among these control
techniques, the reinforcement learning (RL) method seems distinctive and applicable. The
success of the reinforcement learning method in many artificial intelligence applications has
given an explicit indication of how this method might be used to model and adjust occupant
behavior in building control. Fruitful algorithms complement each other and guarantee the
quality of the optimization. However, the examination of occupant behavior based on
reinforcement learning methodologies is not well established. The way that occupant
interacts with the RL agent is still unclear. This study briefly reviews the empirical
applications using reinforcement learning, how they have contributed to shaping the
modeling paradigms and how they might suggest a future research direction. Keywords: Reinforcement learning | occupant behavior | energy efficiency | building control | smart building |
مقاله انگلیسی |
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Optimizing energy consumption and occupants comfort in open-plan offices using local control based on occupancy dynamic data
بهینه سازی مصرف انرژی و آسایش سرنشینان در دفاتر طرح باز با استفاده از کنترل محلی بر اساس اشغال داده های پویا -2020 Optimal management of buildings’ energy-consuming systems is of great importance for minimizing building
energy consumption while satisfying the occupants. Since the operation of building systems are highly dependent
on the presence of occupants, considering the dynamic occupancy information has become crucial to reflect the
occupancy dynamism within offices and the random patterns in occupant behavior. Thus, occupancy-centered
control strategies are required in order to enhance the energy management of buildings. On the other hand,
the need for localized and customizable comfort controls is increasing in office buildings to improve the occupants’
satisfaction, and consequently their productivity. To this end, a framework aiming at developing optimal
occupancy-centered local control strategies is proposed in this paper. A new simulation-based multi-objective
optimization model of the energy consumption in offices is developed to exploit occupancy-related data and
evaluate possible local control strategies to select the best ones. A set of real occupancy data collected over a
period of one year is fed to the integrated simulation-based optimization model for investigating the energysaving
potentials. Comparing the results shows that a considerable improvement in the indoor comfort condition
can be achieved through the application of the proposed framework. We conclude that optimal control
strategies not only provide demand-driven control solutions but also optimize building energy performance. The
integrated model enables dynamic building energy management according to dynamic occupancy patterns. It
avoids over-conditioning that is the result of the application of common practices, which control building systems
based on the peak occupancy. Keywords: Energy management system | Local control strategies | Dynamic occupancy profiles | Comfort | Multi-objective optimization | Simulation |
مقاله انگلیسی |
4 |
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 |
5 |
A study on temperature-setting behavior for room air conditioners based on big data
مطالعه رفتار تنظیم دما برای سیستم های تهویه مطبوع اتاق بر اساس داده های بزرگ-2020 The set-point temperature of room air conditioners (RACs) is extremely critical for cooling
energy consumption of residential buildings. However, current research on temperature-setting
behavior is limited owing to the limitations of data acquisition. This study aims to identify the
typical temperature-setting patterns for RACs and explore the association of temperature-setting
behavior with other RAC operation characteristics. The data obtained from the big data cloud
platform of an RAC manufacturer were analyzed in this study. These data consist of measured
data from 966 bedroom RACs (BRACs) and 321 living room RACs (LRACs). First, the RAC
operation characteristics, involving five parameters, namely, set-point temperature, set wind speed,
indoor temperature, operation duration, and energy consumption, were extracted from the raw data
by transforming, aggregating, and merging the bottom-level measured data. Subsequently, cluster
analysis was performed to identify various and typical temperature-setting behavior patterns. Five
typical temperature-setting patterns for BRACs and six typical patterns for LRACs were obtained.
Afterwards, data mining methods of difference analysis and association analysis were employed to explore the differences and association, respectively, of different temperature-setting patterns with
other operation characteristics of RACs (e.g., set wind speed, indoor air temperature, operation
duration, and energy consumption). The results of this study can provide researchers with
references of temperature-setting strategies in residential building energy simulation and quantify
the energy impacts of diverse temperature-setting patterns in residential buildings. Keywords: Occupant behavior | Room air conditioner | Set-point temperature | Data mining | Cluster analysis |
مقاله انگلیسی |
6 |
A data mining-based method for revealing occupant behavior patterns in using mechanical ventilation systems of Dutch dwellings
یک روش مبتنی بر داده کاوی برای آشکار کردن الگوهای رفتاری سرنشینان در استفاده از سیستمهای تهویه مکانیکی مسکن هلند-2019 Occupant behaviors influence the energy consumption of dwelling mechanical ventilation systems sig- nificantly. There is still a lack of effective method to analyze the occupant behaviors in adjusting the mechanical ventilation systems in buildings. Therefore, this study proposes a data mining-based method to reveal the occupant behavior patterns and the motivations behind. A first derivative Gaussian filter- based approach is developed to detect when an occupant increases or decreases the mechanical venti- lation flowrate without direct measurements. A logistic regression-based statistical analysis approach is developed to find the crucial factors influencing the behaviors of increasing and decreasing ventilation flowrate. A K-means clustering-based analysis approach is introduced to further find the motivations be- hind the behaviors. The proposed data mining-based method discovers the ventilation behaviors and the crucial factors influencing them successfully for the occupants from the 10 dwellings located in a Dutch community. The motivation patterns of the ventilation flowrate adjustment behaviors are further revealed based on the discovered crucial factors. The discovered insights are useful to provide more accurate as- sumptions and inputs for the mechanical ventilation system models. It is also helpful to generate tailored design, refurbishment and control strategies. Keywords: Data mining | Occupant behavior pattern | Mechanical ventilation system | Dwelling |
مقاله انگلیسی |
7 |
Systematic data mining-based framework to discover potential energy waste patterns in residential buildings
چارچوب مبتنی بر داده کاوی سیستمیک برای کشف الگوهای احتمالی پسماندهای انرژی در ساختمانهای مسکونی-2019 Energy feedback systems are recently proposed to help occupants understand and improve their energy use behavior. Despite many potential benefits, the question remains, whether useful and straightforward knowledge are transferred to the occupants about their energy use patterns. In this context, the key is to develop methodologies that can effectively analyze occupants’ energy use behavior and distinguish their energy-inefficient behavior (if any). Previous studies seldom considered the dynamics of occupancy, which may result in misleading information to the occupants and inefficacy in recognizing the actual wasteful behavior. To fill this gap, this study proposes a data mining framework with a combination of change point analysis (CPA), cluster analysis, and association rule mining (ARM) to explore the relation- ship between occupancy and building energy consumption, aiming at identifying potential energy waste patterns and to provide useful feedback to the occupants. To demonstrate the capability of the developed framework, it was applied to datasets collected from two different apartments located in Lyon, France. Results indicate that different energy waste patterns can be effectively discovered in both apartments through the proposed framework and a substantial amount of energy savings can be achieved by modi- fying occupants’ energy use behavior. The proposed framework is flexible and can be adaptive to house- holds with different occupancy patterns and habitual energy-use behavior. Nevertheless, the discovered energy saving potentials and benchmark values are limited to the apartments considered in this study and similar analysis based on the proposed framework are needed in wider building stocks to explore its generalizability. Keywords: Residential buildings | Occupant behavior | Data mining | Energy savings | Feedback |
مقاله انگلیسی |
8 |
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 |
9 |
Development of building energy saving advisory: A data mining approach
توسعه مشاوره صرفه جویی در انرژی ساختمان: رویکرد داده کاوی-2018 Occupants’ behavior and their interaction with home appliances are crucial for assessing building energy
consumption. This study proposes a new methodology for monitoring the energy consumed in building
end-use loads to build an advisory system. The built system alerts occupants to take certain measures
(prioritized recommendations) to reduce energy consumption of end-use loads. The quantification of po
tential savings is also provided upon following said measures. The proposed methodology is also capable
of evaluating the energy savings performed by the occupants. The system works based on the analysis
of historical data generated by occupants using data mining techniques to output highly feasible recom
mendations. For demonstration purposes, the methodology was tested on the real dataset of a building
in Japan. The dataset includes detailed energy consumption of end-use loads, categorized as hot water
supply, lighting, kitchen, refrigerator, entertainment & information, housework & sanitary, and others. Re
sults suggest that the developed models are accurate, and that it is possible to save up to 21% of total
energy consumption by only changing occupants’ energy use habits.
Keywords: Occupant behavior ، Data mining ، Building energy |
مقاله انگلیسی |
10 |
Synthesizing building physics with social psychology: An interdisciplinary framework for context and occupant behavior in office buildings
سنتز فیزیک ساختمان با روانشناسی اجتماعی: یک چارچوب میان رشته ای برای زمینه رفتار ساکنان ساختمان اداری-2017 This study introduces an interdisciplinary framework for investigating building-user interaction in office spaces.
The framework is a synthesis of theories from building physics and social psychology including social cognitive
theory, the theory of planned behavior, and the drivers-needs-actions-systems ontology for energy-related be
haviors. The goal of the research framework is to investigate the effects of various behavioral adaptations and
building controls (i.e., adjusting thermostats, operating windows, blinds and shades, and switching on/off ar
tificial lights) to determine impacts on occupant comfort and energy-related operational costs in the office
environment. This study attempts to expand state-of-the-art understanding of: (1) the environmental, personal,
and behavioral drivers motivating occupants to interact with building control systems across four seasons, (2)
how occupants’ intention to share controls is influenced by social-psychological variables such as attitudes,
subjective norms, and perceived behavioral control in group negotiation dynamic, (3) the perceived ease of
usage and knowledge of building technologies, and (4) perceived satisfaction and productivity. To ground the
validation of the theoretical framework in diverse office settings and contexts at the international scale, an
online survey was designed to collect cross-country responses from office occupants among 14 universities and
research centers within the United States, Europe, China, and Australia.
Keywords: Interdisciplinary framework | Occupant behavior survey | Office buildings | DNAS framework | Social cognitive theory | Theory of planned behavior |
مقاله انگلیسی |