Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Prediction Models of Skin Temperatures and Heat Loss by Evaporation for Thermal Comfort in Buildings in Hot and Humid Climates in Cameroon

The aim of this study is to propose models for predicting skin temperatures and heat loss by evaporation for the inclusion in the calculations of thermal comfort indicators in hot and humid areas, more particularly in sub-Saharan Africa. This will make it possible to complete the thermal comfort data for this climatic region, which for lack of it still uses the standard based on Fanger models, established mainly for the temperate zone (ISO 7730). The experiments were carried out on a representative sample of 24 people (men and women) in experimental buildings, located in the Douala-Cameroon region, representative of the hot and humid zone, as considered by numerous thermal balance references encountered in the litterature. The measurements of the ambient parameters and of the physiological parameters were carried out according to the recommended standards. 1008 skin temperature measurement points were performed on 3 levels of metabolic activity, in order to provide 72 individual average skin temperature values. Analyzes, statistical validation tests and comparisons were performed. We are able to present the most suitable prediction models, other than those of Fanger, for thermal comfort conditions in air-conditioned buildings in hot and humid areas of sub-Saharan Africa. It appears that the skin of people living in these regions has a higher thermal inertia, less water loss by diffusion or a higher skin barrier than that of people in temperate regions

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

2010-2011 > Academic research: refereed > Chapter in an edited book (author

Analysis of Influencing Factors of Green Building Energy Consumption Based on Genetic Algorithm

With the advancement of modernization, high energy consumption buildings can no longer meet the needs of social development. Under the background of low carbon and energy saving, the development of green buildings has become the only way, but its energy-saving design effect needs to be further studied. Aiming at lighting and energy consumption, this study carried out multi-factor optimization analysis based on genetic algorithm on factors such as windowing ratio, wall heat transfer coefficient, window heat transfer coefficient, window transmittance and roof insulation coefficient. Firstly, the theory and technical scheme of applying data mining technology to solve the energy-saving design problems of different buildings are proposed and implemented, including the design of new and existing buildings, as well as the determination of decisive parameters and non-decisive parameters. Secondly, computer simulation and theoretical analysis are used to optimize the analysis of the building scheme, so as to find the optimal design range of each influencing factor and the optimal design method of green low-energy building. Multi-factor optimization theory and genetic algorithm principle are summarized, and the heat transfer coefficient of external wall and window of the building is selected as the optimization variable, so as to achieve low energy consumption and enclosure cost of the building. Aiming at better thermal comfort, an optimization model was established. Finally, through empirical research, an energysaving plan was designed, and genetic algorithm was used to obtain the optimal solution for maximizing the incremental benefits obtained by unit input incremental cost. The results indicate that the ideal incremental benefits come from a reasonable and effective combination of technologies, mainly from air conditioning systems and lighting systems; the setting of the benchmark return rate will directly affect the optimization effect of energy-saving plans, providing decision-makers with the optimal combination of energy-saving technologies

Overall effects of temperature steps in hot summer on students' subjective perception, physiological response and learning performance

University students are very likely to experience temperature steps before class in hot summer. This study aims to investigate the overall effects of step changes on students' subjective perception, physiological response and learning performance, so as to explore an optimal thermal condition for classrooms in hot summer. Four typical temperature step conditions (S6: 34 °C-28 °C, S8: 34 °C-26 °C, S10: 34 °C-24 °C, S12: 34 °C–22 °C) were developed to conduct experiments on sixteen participants. It has been found that after temperature steps, no more than 62.5% of students consistently found thermally acceptable at 22 °C; students felt the most acceptable and comfortable at 26 °C; the effect of thermal environment on workload was not significant in most cases, especially for memory-related tasks; students' negative mood was less at 26 °C than at 28 °C and 22 °C. When the temperature step was less than S12, blood pressure and blood oxygen saturation were insensitive to temperature steps; core temperature continued to rise during the first 5 min and then decreased significantly when the temperature step exceeded S8. No significant difference in learning performance was found among the four conditions; the differences in relative performance between thermal conditions were <2%, and are not likely to have practical meaning in building management practice. Overall, the optimal thermal condition is 26 °C, and it is recommended to set the indoor temperature between 24 and 28 °C

The Adaptive Thermal Comfort Review from the 1920s, the Present, and the Future

The typical method for comfort analysis is the Predicted Mean Vote and Predicted Percentage Dissatisfied (PMV-PPD). However, they present limitations in accommodating the comfort of a disabled and elder group of people, which are the most vulnerable to climate change and energy poverty. The adaptive method can give flexibility and personalisation needed to overcome the problem due to the variability of the people's metabolism, historical and behavioural preferences. Investments to upgrade the indoor environmental quality and building design can then be effectively used and, for the first time, it will be possible to tailor the solutions for these particular groups of people. The adaptive approach uses Artificial Intelligence (AI), where it can introduce the imperfect learning process. Overcoming this, instead of going further for the Explainable AI, the PMV–PPD approach can be used for the learning validation and verification needed for the adaptive setting point and standards

Analysis of strategies to reduce thermal discomfort and natural gas consumption during heating season in Algerian residential dwellings

In the Algerian building sector, the heating needs are essentially satisfied with fossil fuels (in particular natural gas). The more common heating system in multifamily buildings is a gas heater in the central corridor of each dwelling. This system can cause important overheating in the corridor and significant gas consumption. The present study evaluates the energy savings and thermal comfort improvement, for three different cities of Algeria, achieved with a different heating system based on hot water radiators. In situ measurements were performed in a typical dwelling (which served as a reference case) and the results were used to calibrate and validate the TRNSYS model that was used for this study. A parametric analysis was performed by varying the location, heating system, envelope and windows. It was found that among the scenarios tested, it was possible to substantially reduce the heating needs compared to the reference dwelling and that the number of hours of thermal discomfort could be virtually eliminated. The most influential parameters affecting these model outputs appeared to be the wall thermal insulation

An artificial intelligence platform for design optimization and data analysis: application for fire and ventilation problems

This thesis focuses on the development of novel multi-objective software platforms to assist engineering design and investigation, especially for simulation-based indoor environment problems, which always involve multiple evaluation criteria. In addition, this thesis aims to develop new methods to reduce the computational cost associated with the design process. In modern building design, engineers are constantly facing challenging to find an optimal design to maintain a high level of thermal comfort and indoor air quality for occupants while minimizing the system energy consumption. Over the past decades, several algorithms have been proposed and developed for optimizing the heating, ventilation and air conditioning (HVAC) system for indoor environment. Nevertheless, the majority of these optimization algorithms are focused on single objective optimization procedures and require a large training sample for surrogate modelling. For multi-objective HVAC design problems, previous studies introduced an arbitrary weighting factor to combine all design objectives into one single objective function. The near-optimal solutions were however sensitive to the chosen value of the weighting factor. In another hand, the computational cost is very heavy in the computer-aided investigation process of reverse engineering problems. Computational Fluid Dynamics (CFD) aided fire investigation is one of the reverse engineering. With the significant growth of the world population, our cities are becoming more and more crowding. In this situation, any fire occurring would cause severe consequences, including property damage and human injuries or even deaths. In assessing the fire cause, the fire origin determination is a crucial step identifying the origin of fire outbreak and the sequential fire and smoke propagation. Traditionally, fire investigators relied upon the visible fire damages at the fire scene to determine the location of fire originated based on their own professional experience. The fire investigation process is however subject to the expert interpretation inherently embedded in the qualitative analyses. In addition, we are living in an era of big data, where lots amount of data are generating every day, especially in engineering field. Traditional analysis methods are not suitable to handle large amount of data quickly and accurately. In contrast, new techniques such as machine learning are able to deal with big data and extract data features. The main body of this thesis is composed of seven chapters, and the details of each chapter are as the followings: The research background and a comprehensive literature review are described in the first two chapters where the research gaps found in the existing literatures are discussed. From Chapter 3 to Chapter 6, the main contributions of this research are demonstrated. In Chapter 3, a nondominated sorting-based particle swarm optimization (NSPSO) algorithm together with the Kriging method to perform optimization for the HVAC system design of a typical office room was developed. In addition, an adaptive sampling procedure was also introduced to enable the optimization platform to adjust the sampling point and resolution in constructing the training sample. Chapter 4 presents a Multi-fidelity Kriging algorithm to quantitatively determine the fire origin based on the soot deposition patterns predicted by the numerical simulations, which provides an unbiased and fast methodology to assist the fire investigation. A comprehensive multi-objective optimization platform of the ventilation system inside a typical high-speed train (HST) cabin is discussed in Chapter 5, where the NSPSO and the Multi-fidelity Kriging were combined together to reduce computational cost. Chapter 6 demonstrates a successful application of convolutional neural networks (CNN) in vegetation feature analysis to help cut powerline wildfire risk caused by vegetation conduction ignition. Finally, all the contributions in this research are summarised in Chapter 7