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

Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Selected Papers from IEEE ICASI 2019

The 5th IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on 11–15 April, 2019, provided a unified communication platform for a wide range of topics. This Special Issue entitled “Selected Papers from IEEE ICASI 2019” collected nine excellent papers presented on the applied sciences topic during the conference. Mechanical engineering and design innovations are academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. These new technologies that implant intelligence in machine systems represent an interdisciplinary area that combines conventional mechanical technology and new IT. The main goal of this Special Issue is to provide new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology

Deep Learning Techniques for Power System Operation: Modeling and Implementation

The fast development of the deep learning (DL) techniques in the most recent years has drawn attention from both academia and industry. And there have been increasing applications of the DL techniques in many complex real-world situations, including computer vision, medical diagnosis, and natural language processing. The great power and flexibility of DL can be attributed to its hierarchical learning structure that automatically extract features from mass amounts of data. In addition, DL applies an end-to-end solving mechanism, and directly generates the output from the input, where the traditional machine learning methods usually break down the problem and combine the results. The end-to-end mechanism considerably improve the computational efficiency of the DL.The power system is one of the most complex artificial infrastructures, and many power system control and operation problems share the same features as the above mentioned real-world applications, such as time variability and uncertainty, partial observability, which impedes the performance of the conventional model-based methods. On the other hand, with the wide spread implementation of Advanced Metering Infrastructures (AMI), the SCADA, the Wide Area Monitoring Systems (WAMS), and many other measuring system providing massive data from the field, the data-driven deep learning technique is becoming an intriguing alternative method to enable the future development and success of the smart grid. This dissertation aims to explore the potential of utilizing the deep-learning-based approaches to solve a broad range of power system modeling and operation problems. First, a comprehensive literature review is conducted to summarize the existing applications of deep learning techniques in power system area. Second, the prospective application of deep learning techniques in several scenarios in power systems, including contingency screening, cascading outage search, multi-microgrid energy management, residential HVAC system control, and electricity market bidding are discussed in detail in the following 2-6 chapters. The problem formulation, the specific deep learning approaches in use, and the simulation results are all presented, and also compared with the currently used model-based method as a verification of the advantage of deep learning. Finally, the conclusions are provided in the last chapter, as well as the directions for future researches. It’s hoped that this dissertation can work as a single spark of fire to enlighten more innovative ideas and original studies, widening and deepening the application of deep learning technique in the field of power system, and eventually bring some positive impacts to the real-world bulk grid resilient and economic control and operation

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Sviluppo di un metodo innovativo per la misura del comfort termico attraverso il monitoraggio di parametri fisiologici e ambientali in ambienti indoor

openLa misura del comfort termico in ambienti indoor è un argomento di interesse per la comunità scientifica, poiché il comfort termico incide profondamente sul benessere degli utenti ed inoltre, per garantire condizioni di comfort ottimali, gli edifici devono affrontare costi energetici elevati. Anche se esistono norme nel campo dell'ergonomia del comfort che forniscono linee guida per la valutazione del comfort termico, può succedere che in contesti reali sia molto difficile ottenere una misurazione accurata. Pertanto, per migliorare la misura del comfort termico negli edifici, la ricerca si sta concentrando sulla valutazione dei parametri personali e fisiologici legati al comfort termico, per creare ambienti su misura per l’utente. Questa tesi presenta diversi contributi riguardo questo argomento. Infatti, in questo lavoro di ricerca, sono stati implementati una serie di studi per sviluppare e testare procedure di misurazione in grado di valutare quantitativamente il comfort termico umano, tramite parametri ambientali e fisiologici, per catturare le peculiarità che esistono tra i diversi utenti. In primo luogo, è stato condotto uno studio in una camera climatica controllata, con un set di sensori invasivi utilizzati per la misurazione dei parametri fisiologici. L'esito di questa ricerca è stato utile per ottenere una prima accuratezza nella misurazione del comfort termico dell'82%, ottenuta mediante algoritmi di machine learning (ML) che forniscono la sensazione termica (TSV) utilizzando la variabilità della frequenza cardiaca (HRV) , parametro che la letteratura ha spesso riportato legato sia al comfort termico dell'utenza che alle grandezze ambientali. Questa ricerca ha dato origine a uno studio successivo in cui la valutazione del comfort termico è stata effettuata utilizzando uno smartwatch minimamente invasivo per la raccolta dell’HRV. Questo secondo studio consisteva nel variare le condizioni ambientali di una stanza semi-controllata, mentre i partecipanti potevano svolgere attività di ufficio ma in modo limitato, ovvero evitando il più possibile i movimenti della mano su cui era indossato lo smartwatch. Con questa configurazione, è stato possibile stabilire che l'uso di algoritmi di intelligenza artificiale (AI) e il set di dati eterogeneo creato aggregando parametri ambientali e fisiologici, può fornire una misura di TSV con un errore medio assoluto (MAE) di 1.2 e un errore percentuale medio assoluto (MAPE) del 20%. Inoltre, tramite il Metodo Monte Carlo (MCM) è stato possibile calcolare l'impatto delle grandezze in ingresso sul calcolo del TSV. L'incertezza più alta è stata raggiunta a causa dell'incertezza nella misura della temperatura dell'aria (U = 14%) e dell'umidità relativa (U = 10,5%). L'ultimo contributo rilevante ottenuto con questa ricerca riguarda la misura del comfort termico in ambiente reale, semi controllato, in cui il partecipante non è stato costretto a limitare i propri movimenti. La temperatura della pelle è stata inclusa nel set-up sperimentale, per migliorare la misurazione del TSV. I risultati hanno mostrato che l'inclusione della temperatura della pelle per la creazione di modelli personalizzati, realizzati utilizzando i dati provenienti dal singolo partecipante, porta a risultati soddisfacenti (MAE = 0,001±0,0003 e MAPE = 0,02%±0,09%). L'approccio più generalizzato, invece, che consiste nell'addestrare gli algoritmi sull'intero gruppo di partecipanti tranne uno, e utilizzare quello tralasciato per il test, fornisce prestazioni leggermente inferiori (MAE = 1±0.2 e MAPE = 25% ±6%). Questo risultato evidenzia come in condizioni semi-controllate, la previsione di TSV utilizzando la temperatura della pelle e l'HRV possa essere eseguita con un certo grado di incertezza.Measuring human thermal comfort in indoor environments is a topic of interest in the scientific community, since thermal comfort deeply affects the well-being of occupants and furthermore, to guarantee optimal comfort conditions, buildings must face high energy costs. Even if there are standards in the field of the ergonomics of the thermal environment that provide guidelines for thermal comfort assessment, it can happen that in real-world settings it is very difficult to obtain an accurate measurement. Therefore, to improve the measurement of thermal comfort of occupants in buildings, research is focusing on the assessment of personal and physiological parameters related to thermal comfort, to create environments carefully tailored to the occupant that lives in it. This thesis presents several contributions to this topic. In fact, in the following research work, a set of studies were implemented to develop and test measurement procedures capable of quantitatively assessing human thermal comfort, by means of environmental and physiological parameters, to capture peculiarities among different occupants. Firstly, it was conducted a study in a controlled climatic chamber with an invasive set of sensors used for measuring physiological parameters. The outcome of this research was helpful to achieve a first accuracy in the measurement of thermal comfort of 82%, obtained by training machine learning (ML) algorithms that provide the thermal sensation vote (TSV) by means of environmental quantities and heart rate variability (HRV), a parameter that literature has often reported being related to both users' thermal comfort. This research gives rise to a subsequent study in which thermal comfort assessment was made by using a minimally invasive smartwatch for collecting HRV. This second study consisted in varying the environmental conditions of a semi-controlled test-room, while participants could carry out light-office activities but in a limited way, i.e. avoiding the movements of the hand on which the smartwatch was worn as much as possible. With this experimental setup, it was possible to establish that the use of artificial intelligence (AI) algorithms (such as random forest or convolutional neural networks) and the heterogeneous dataset created by aggregating environmental and physiological parameters, can provide a measure of TSV with a mean absolute error (MAE) of 1.2 and a mean absolute percentage error (MAPE) of 20%. In addition, by using of Monte Carlo Method (MCM), it was possible to compute the impact of the uncertainty of the input quantities on the computation of the TSV. The highest uncertainty was reached due to the air temperature uncertainty (U = 14%) and relative humidity (U = 10.5%). The last relevant contribution obtained with this research work concerns the measurement of thermal comfort in a real-life setting, semi-controlled environment, in which the participant was not forced to limit its movements. Skin temperature was included in the experimental set-up, to improve the measurement of TSV. The results showed that the inclusion of skin temperature for the creation of personalized models, made by using data coming from the single participant brings satisfactory results (MAE = 0.001±0.0003 and MAPE = 0.02%±0.09%). On the other hand, the more generalized approach, which consists in training the algorithms on the whole bunch of participants except one, and using the one left out for the test, provides slightly lower performances (MAE = 1±0.2 and MAPE = 25%±6%). This result highlights how in semi-controlled conditions, the prediction of TSV using skin temperature and HRV can be performed with acceptable accuracy.INGEGNERIA INDUSTRIALEembargoed_20220321Morresi, Nicol