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Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilat...
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Building operations consume approximately 40% of global energy, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for up to 50% of this consumption. As HVAC energy demands are expected to rise, optimising system efficiency is crucial for reducing future energy use and mitigating climate change. Existing control strategies l...
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Heating, Ventilation and Air Conditioning (HVAC) control and energy usage management has been identified as a promising way of improving building energy efficiency and thus contributing to solving the energy challenges of the world. However, a critical aspect of reducing HVAC energy usage is fault detection and diagnosis (FDD). Several studies have...
We present evaluation of actual use of an occupancy-reactive space heating control, which changes set-point temperatures in space heating for energy-savings based on changes in occupancy state. We performed an experiment over two winter months in Lyon, France. In this experiment, occupants were provided with occupancy-reactive and pre-scheduled con...
Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this...
Heating, ventilation, and air conditioning (HVAC) systems are major contributors to building energy consumption, and their attention has been drawn to them by the growing need for energy-efficient solutions in contemporary architecture. HVAC systems are essential for ensuring comfort and reducing energy consumption in smart buildings. Incorporating...
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... Black-box models include supervised learning, unsupervised learning (e.g., clustering for data labelling), and transfer learning, which applies pre-built models from other buildings to a target building. • Grey-box models [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] combine the principles of white-box and black-box models and use parameter estimation techniques to improve model accuracy while reducing data requirements. These models can effectively reuse pre-built models from past applications or other buildings and are particularly advantageous in environments with limited data. ...
... In the study by Bahramnia et al. [74] model predictive control (MPC) optimized the control of temperature and humidity in HVAC systems, achieving improvements in energy efficiency and comfort. Although the Ecuadorian study does not focus on control system applications, the analysis of interactions between temperature and relative humidity through ARIMA compares favorably in terms of overall accuracy. ...
Artificial intelligence (AI) has established itself as an essential tool in climatology. It facilitates accurate analysis and prediction of variables such as temperature and humidity, which are crucial for understanding global warming and its effects. In this context, this study aims to implement predictive simulations of temperature and relative humidity on the Ecuadorian coast using artificial intelligence (AI). This study adopts a quantitative methodology, utilizing daily historical data collected from 2015 to 2020. Monthly averages for maximum temperature and relative humidity were calculated, based on 72 observations for each variable. The climate simulation employed statistical techniques such as linear regression and simple correlation, along with the implementation of various AI libraries in Rstudio, including readxl, QuantPsyc, and ggplot2, among others. Additionally, the ARIMA model was applied to analyze time series, facilitating detailed simulation and prediction of both climatic variables. The results indicate a significant inverse correlation between maximum temperature and relative humidity, revealing high-temperature variability in recent years. The optimized ARIMA predictive models showed AICC indices of 180.47 for temperature and 283.16 for humidity, after 96 iterations, demonstrating the high reliability of AI in climate prediction for the Ecuadorian coastal region. The study concludes with the importance of integrating advanced technologies such as AI in climatology to improve the accuracy and efficiency of climate predictions.
... Other studies [26] have focused on hygrothermal interactions between indoor air and heat transfer through the building envelope, incorporating the main sources of moisture into the model. Moreover, a model was developed for simultaneous control of temperature and humidity [27], resulting in a control strategy that optimizes tracking error while minimizing control effort. These models require detailed instrumental analysis to improve the interaction with the building under study and to propose improvements based on a physical analysis of the infrastructure and the outcomes of models. ...
Estimating energy flows that affect temperature increases inside houses is crucial for optimizing building design and enhancing the comfort of living spaces. In this study, a thermal model has been developed to estimate the internal temperature of rural houses in Mexico using aerial thermography. The methodology used in this study considered three stages: (a) generating a semi-experimental thermal model of heat transfer through roofs for houses with high infiltration, (b) validating the model using contact thermometers in rural community houses, and (c) integrating the developed model using aerial thermography and Python 3.11.4 into user-friendly software. The results demonstrate that the thermal model is effective, as it was tested on two rural house configurations and achieved an error margin of less than 10% when predicting both maximum and minimum temperatures compared to actual measurements. The model consistently estimates the internal house temperatures using aerial thermography by measuring the roof temperatures. Experimental comparisons of internal temperatures in houses with concrete and asbestos roofs and the model's projections showed deviations of less than 3 • C. The developed software for this purpose relies solely on the fundamental thermal properties of the roofing materials, along with the maximum roof temperature and ambient temperature, making it both efficient and user-friendly for rural community management systems. Additionally, the model identified areas with comfortable temperatures within different sections of a rural community, demonstrating its effectiveness when integrated with aerial thermography. These findings suggest the potential to estimate comfortable temperature ranges in both rural and urban dwellings, while also encouraging the development of public policies aimed at improving rural housing.
... White-box modeling involves creating a system model based on a thorough understanding of the physical rules that define it [14]. In the context of building energy, white-box models simulate thermal behavior using resistance and capacitance networks to represent the building's thermal properties [12,[15][16][17]. White-box models consider factors like weather profiles, building architecture, occupant behavior, and control strategies [18]. ...
... MPC has become a widely adopted advanced control technology in various fields, including building automation and control [33]. For example, Ref. [15] proposed an MPC strategy for simultaneous temperature and humidity control in HVAC systems. The study by Ref. [34] compared predictive control schemes, including an ANN-based approach, emphasizing the importance of training data. ...
Our work is dedicated to enhancing sustainability through improved energy efficiency in buildings, with a specific focus on heating and cooling control and the optimization of thermal comfort of occupants. With an energy consumption of more than 60% in buildings, HVAC systems are the biggest energy users. By integrating advanced technology, data algorithms, and digital twins, our study aims to optimize energy performance effectively. We have developed a Neural Network-based Model Predictive Control (NNMPC) to achieve this goal. Leveraging technologies such as MQTT communication, Wi-Fi modules, and field-programmable gate arrays will enhance scalability and flexibility. Our findings demonstrate the efficacy of the NNMPC system deployed on the PYNQ board for reducing sensible thermal energy usage for both cooling and heating purposes. Compared to traditional On/Off control systems, the NNMPC achieved an impressive 40.8% reduction in heating energy consumption and a 37.8% decrease in cooling energy consumption in 2006. In comparison to the On/Off technique, the NNMPC demonstrated a 25.6% reduction in annual heating energy consumption and a 28.8% drop in annual cooling energy consumption in the simulated year of 2017. We observed that, across all strategies and platforms, there were no instances where the Predicted Mean Vote (PMV) fell below −0.5. However, a significant proportion of PMV values (ranging from 65% to 83%) were observed between −0.5 and 0.5, signifying a high level of occupant comfort. Additionally, for PMV values between 0.5 and 1.0, percentages ranged from 16% to 33% for both years. Importantly, the NNMPC exhibited notable efficiency in maintaining occupants’ comfort within this range, requiring less energy while ensuring highly satisfactory environments.
... Nair, Parekh e Tailor (2018) buscam gerar um modelo de regressão para a predição da condutividade térmica de nanofluidos à base de R718 para cenários de baixa fração volumétrica de partículas, Zhi et al. (2019) estudam melhorias na otimização de desumidificadores dessecantes de líquidos e outras aplicações de plásticos com fluxos de gás líquido, Bahramnia et al. (2019) têm como objetivo fornecer uma estrutura para descrever elementos de temperatura e umidade necessários para modelagem dinâmica. Ma et al. (2020) procuram melhorar o desempenho real e melhorias potenciais do processo de filtragem e ventilação em salas de limpeza, Yin et al. (2020) procuram a melhoria no desempenho do ambiente interno em operação, comparando com os requisitos padrões, Guan et al. (2020) têm como objetivo a melhoria no desempenho de um sistema de ar-condicionado fresco completo baseado no dessecante líquido aplicado em uma fábrica industrial e por fim, Mugnini et al. (2019) buscam destacar destacar as vantagens e questões ligadas à adoção de DCSs para resfriamento predial quando o frio é recuperado de uma aplicação específica. ...
... Apesar de vários estudos acerca de modelos de otimização para ar-condicionado, novos fluídos para melhoria de desempenho, estudo de sensores para melhor coleta de dados, ainda temos algumas oportunidades de pesquisa: 9. Considerar quantidade de dióxido de carbono produzidas nas salas para proporcionar melhores condições de conforto (BAHRAMNIA et al., 2019); 10. Avaliação do impacto do método de dimensionamento do DCS (MUGNINI et al., 2019). ...
... Nair, Parekh e Tailor (2018) buscam gerar um modelo de regressão para a predição da condutividade térmica de nanofluidos à base de R718 para cenários de baixa fração volumétrica de partículas, Zhi et al. (2019) estudam melhorias na otimização de desumidificadores dessecantes de líquidos e outras aplicações de plásticos com fluxos de gás líquido, Bahramnia et al. (2019) têm como objetivo fornecer uma estrutura para descrever elementos de temperatura e umidade necessários para modelagem dinâmica. Ma et al. (2020) procuram melhorar o desempenho real e melhorias potenciais do processo de filtragem e ventilação em salas de limpeza, Yin et al. (2020) procuram a melhoria no desempenho do ambiente interno em operação, comparando com os requisitos padrões, Guan et al. (2020) têm como objetivo a melhoria no desempenho de um sistema de ar-condicionado fresco completo baseado no dessecante líquido aplicado em uma fábrica industrial e por fim, Mugnini et al. (2019) buscam destacar destacar as vantagens e questões ligadas à adoção de DCSs para resfriamento predial quando o frio é recuperado de uma aplicação específica. ...
... Apesar de vários estudos acerca de modelos de otimização para ar-condicionado, novos fluídos para melhoria de desempenho, estudo de sensores para melhor coleta de dados, ainda temos algumas oportunidades de pesquisa: 9. Considerar quantidade de dióxido de carbono produzidas nas salas para proporcionar melhores condições de conforto (BAHRAMNIA et al., 2019); 10. Avaliação do impacto do método de dimensionamento do DCS (MUGNINI et al., 2019). ...
Estamos vivendo na era digital, em que fábricas e suas máquinas equipadas com
inteligência artificial tornam-se inteligentes e com ampla interação humano-máquina. No entanto,
a forma como os indivíduos se apresentam permanece analógica, ou seja, dependem de currículos
e apresentações gravadas ou em papel, principalmente quando não são graduados na área de
tecnologia da informação. Nesse sentido, o objetivo deste trabalho é promover a aplicação de um
repositório digital como ferramenta de armazenamento e fonte de referência para cursos de gestão
no eixo tecnológico da produção industrial, ou seja, para leigos em programação. A metodologia
incluiu pesquisa bibliográfica e de campo com 210 voluntários buscando-se construir e gerar
conhecimento. Em seguida, foi criado um modelo utilizando uma plataforma de hospedagem,
compartilhamento e gerenciamento conhecido como github. Os resultados apontam para um
template digital como produto, uso de tecnologias específicas de suporte e uma mentalidade ágil,
ou seja, errarrápido para consertar mais rápido. Conclui-se que em repositórios atualizados, novos
gestores e outros leigos podem se apresentar digitalmente além de registrar seus projetos e eventos
em ciberespaço com acessibilidade e mobilidade em qualquer lugar e em tempo real.
... The DX refrigeration plant comprises of electronic expansion valve (EEV), changeable speed compressor, DX condenser and evaporator. The DX plant thermodynamic cycle contains the processes of isenthalpic expansion, isentropic compression, rejection and condensation of isobaric heat, and processes of evaporation and absorption of isobaric heat [33]. The evaporator DX was located in the air supply pipe to act as an air cooling coil. ...
This paper describes a novel hybrid technique with fractional order PID controller (FOPID) for simultaneously controlling the humidity of indoor air temperature and the direct expansion (DX) air conditioning (A/C) system. The proposed hybrid system is a joint performance of the butterfly optimization algorithm (BOA) and adaptive network fuzzy inference system (ANFIS), hence forth it is called BOANFIS Technique (BOANFIST). The purpose of the proposed system is to disconnect the temperature and humidity control circuits. The proposed control is modeled and replicated on MATLAB platform and is assessed using existing systems. The statistical performance of the proposed and existing systems of mean, median and standard deviation is also evaluated. It reduces computational time up to 1.01 s and also reduces energy consumption to around 16.42 KWh/day. Furthermore, the simulation outcomes suggest that the proposed technique may efficiently and accurately obtain the optimal global solutions of the proposed technique compared to existing systems.
... A broad spectrum of software that rely on numerical processing for simulating the unsteady thermal performance of buildings has been well established for many decades (DOE, TRNSYS, BLAST, EnergyPlus, Genopt, MatLab/Simulink, SUNCODE, COMSOL), see for instance Refs. [11][12][13][14][15][16][17][18][19][20]. Moreover, mathematical models based on RC electric circuit analogy, adequate for high mass building, can also be found [21][22][23][24]. ...
... Finally, according to equation (15), the system of equations is written as follows. ...
The present work deals with an approximate analytical solution for the dynamic temperature field due to the coupled effect of building walls to the adjacent environment. The solution is derived within the framework of weighted residuals and can be used as a handy tool to realize a reasonable estimation of the building thermal performance and to elucidate the dependence on the room descriptive parameters. Results from the present model are compared with predictions arising from both a commercial FEM code and the analytical solution from the standard EN 13786, showing satisfactory results. This conclusion is not surprising since it is demonstrable in view of the characteristic-time responses of the climatic forcing and the building walls. Finally, as an example of application of this approach, the model was focused on describing the thermal response of an open-loop regulated indoor environment.
... A temperature model of the buildings or rooms to be conditioned is needed for building thermal management and predictive control [7][8]. Temperature models in buildings can be grouped based on their modelling paradigm, i.e. white, black, and gray-box models [9][10][11]. ...
In this paper, a room temperature prediction model was developed using a gray-box approach. Room temperature prediction is an important part in building automation for building energy conservation and efficiency. The gray-box or hybrid model is an approach which combines white-box and black-box modeled components. The white box model in this research was a physics-based model built using electric RC-network analogy in MATLAB Simulink. The black-box model was a data-based model configured to reduce the error of the white-box model and was built using Least Square Boost machine learning algorithm in MATLAB. The gray-box model was trained with data from EnergyPlus simulations. The gray-box model prediction reached a CV(RMSE) <5% and R2 > 0.76 for all the variation of the building constructions tested.
... [12][13][14][15][16][17][18]. As regards this typology, many studies can be found which are based on the MATLAB/Simulink environment to simulate heating plants in terms of temperature control and efficiency, [19][20][21][22][23][24][25][26]. They give surprisingly realistic information on the operation of actual control systems, both in combination with other plants and for different types of buildings, showing the effect of different control strategies. ...
