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

Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository

Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.</jats:p

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning

Seleção de atributos usando árvores de decisão não-binárias

Mestrado em Engenharia Eletrónica e InformáticaExame público realizado em 22 de Maio de 2018A aprendizagem automática, área integrada na inteligência artificial, possui como principal objetivo a criação e o desenvolvimento de métodos e algoritmos que possuam capacidades comummente associadas aos humanos, como a aquisição e a descoberta de novos factos ou conhecimentos. Quando comparado com humanos, as principais vantagens da implementação destes métodos estão normalmente associadas a otimizações temporais e monetárias. Este trabalho apresenta um estudo de seleção de atributos/características e capacidade de previsão/classificação aplicado à monitorização de condições de ferramentas de corte (desgaste de ferramentas) e a classificação de potenciais novos clientes para serviços bancários (telemarketing bancário), usando as árvores de decisão ID3 com a capacidade de lidar com variáveis contínuas – algoritmo adaptado neste trabalho. Os resultados obtidos demonstram que este algoritmo, em comparação com as árvores de decisão convencionais, para conjuntos de dados reduzidos, apresenta o melhor desempenho. A seleção de atributos realizada pelo algoritmo adaptado provou ser uma mais-valia, quer seja para posterior classificação com a aplicação do algoritmo desenvolvido ou com a aplicação de outros algoritmos de referência na área de aprendizagem automática. Os resultados obtidos dos conjuntos de dados do desgaste de ferramentas e do telemarketing bancário apresentam uma redução de 15 para 5 e de 19 para 15 atributos, respetivamente. Em ambos os estudos ficou demonstrada a eficácia desta abordagem bem como a aplicabilidade na seleção de atributos de forma simples e transparente, mesmo na presença de dados com ruído.Machine learning, an area integrated in artificial intelligence, has as main objective the creation and development of methods and algorithms that have abilities commonly associated with humans, such as the acquisition and discovery of new facts or knowledge. When compared to humans, the main advantages of implementing these methods are usually associated with temporal and monetary optimizations. To this end, there are several models/algorithms, such as decision trees, neural networks and support vector machines, performing tasks that can also be different, such as classification and selection of attributes. In order to overcome inherent limitations to the ID3 decision trees, in relation to the manipulation of continuous variables and viability test, in this work an adaptation of the original algorithm was developed and implemented, using the same metrics, allowing, however, its application in data sets with continuous variables. This work presents a study of selection of attributes/characteristics and prediction/classification capacity applied to the monitoring of cutting tool conditions (tool wear) and the classification of potential new clients for banking services (banking telemarketing) using ID3 decision with the ability to handle continuous variables. The results show that this algorithm, in comparison to the conventional decision trees, namely the algorithms C4.5, CART and Random Forest, for reduced datasets, presents the best performance, with an improvement of 12.5% to 25%. For large data sets, despite having the lowest rating value, the difference is not at all relevant (-2%). The developed algorithm stands out because it allows a detailed analysis, contrary to C4.5 and CART that allow a general analysis. This is due to the way algorithms deal with and perform divisions when working with continuous variables. The selection of attributes performed by the adapted algorithm proved to be an asset, either for later classification with the application of the developed algorithm or with the application of other reference algorithms in the area of machine learning. The results obtained from tool wear data sets and bank telemarketing show a reduction from 15 to 5 and from 19 to 15 attributes, respectively. The applicability of decision trees has been proven both in the monitoring of multisensor processes and in the classification of new clients with continuous variables. This approach also revealed that decision trees can be applied for the purpose of selecting attributes in a simple and transparent way, even in the presence of noise data

Open Data and Models for Energy and Environment

This Special Issue aims at providing recent advancements on open data and models. Energy and environment are the fields of application.For all the aforementioned reasons, we encourage researchers and professionals to share their original works. Topics of primary interest include, but are not limited to:Open data and models for energy sustainability;Open data science and environment applications;Open science and open governance for Sustainable Development Goals;Key performance indicators of data-aware energy modelling, planning and policy;Energy, water and sustainability database for building, district and regional systems; andBest practices and case studies

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems