8,134 research outputs found
An Expandable, Contextualized and Data-Driven Indoor Thermal Comfort Model
Continuous discrepancies in building performance predictions creates an ongoing inclination to link contextualized, real-time inputs and users’ feedback for not only building control systems but also for simulation tools. It is now seeming necessary to develop a model that can record, find meaningful relationship and predict more holistic human interactions in buildings. Such model could create capacity for feedback and control with a level of intelligence. Fuzzy Logic Systems (FLSs) are known as robust tools in decision making and developing models in an efficient manner. Considering this capability, in this paper, FLSs is implemented to make a thermal comfort model in an educational building in the UK. Such implementation has an ability to respond to some identified desires of developers and performance assessors in addressing uncertainty in thermal comfort models. The results demonstrate the proposed method is practical to simulate the value of comfort level based on the input data
An ARTMAP-incorporated Multi-Agent System for Building Intelligent Heat Management
This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach
Thermal comfort based fuzzy logic control
Most heating, ventilation and air conditioning (HVAC) control systems are considered as temperature control problems. In this work, the predicted mean vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. This is achieved through the use of a fuzzy logic controller that takes into account a range of human comfort criteria in the formulation of the control action that should be applied to the heating system to bring the space to comfort conditions. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. Simulation results show that the proposed control strategy makes it possible to maximize the indoor thermal comfort and, correspondingly, a reduction in energy use of 20% was obtained for a typical 7-day winter period when compared with conventional control
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning
The conventional control paradigm for a heat pump with a less efficient
auxiliary heating element is to keep its temperature set point constant during
the day. This constant temperature set point ensures that the heat pump
operates in its more efficient heat-pump mode and minimizes the risk of
activating the less efficient auxiliary heating element. As an alternative to a
constant set-point strategy, this paper proposes a learning agent for a
thermostat with a set-back strategy. This set-back strategy relaxes the
set-point temperature during convenient moments, e.g. when the occupants are
not at home. Finding an optimal set-back strategy requires solving a sequential
decision-making process under uncertainty, which presents two challenges. A
first challenge is that for most residential buildings a description of the
thermal characteristics of the building is unavailable and challenging to
obtain. A second challenge is that the relevant information on the state, i.e.
the building envelope, cannot be measured by the learning agent. In order to
overcome these two challenges, our paper proposes an auto-encoder coupled with
a batch reinforcement learning technique. The proposed approach is validated
for two building types with different thermal characteristics for heating in
the winter and cooling in the summer. The simulation results indicate that the
proposed learning agent can reduce the energy consumption by 4-9% during 100
winter days and by 9-11% during 80 summer days compared to the conventional
constant set-point strategyComment: Submitted to Energies - MDPI.co
Control of HVAC system comfort by sampling
The sampling of the users comfort, allows observing and predicting the level of comfort on the HVAC (heating,
ventilation, and air conditioning) systems. The development of online sampling systems assists in the recognition of
the behavior patterns that occur in the offices.
This paper presents a user-friendly tool designed and developed in order to make easier knowledge extraction and
representation to make possible decisions about which demand that must prevail, the user comfort or saving into a
central system. This decision may depend on the occupation and feeling of comfort of its occupants.
Some studies have put neutral thermal conditions outside the ranges of comfort of the ASHRAE standard. The actual
rules of the HVAC systems are based on studies carried out on specific populations in a specific space, which are not
valid in certain situations. This is a dynamic idea of the comfort based in real data.
The methodology used provides important and useful information to be able to select the comfort set-point of the
rooms of a central heating system without the need to use fixed values based on programmed time schedules or any
other methodology. The response to comfort in an area of a building throughout the day can be seen in this study.
The users were assessed using a standard set of key questions in order to measure the level of satisfaction with
environmental factors, thanks to a questionnaire of imprecise answers. We seek an improvement in the building users,
regardless of their particularities
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