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

AI for Improving the Overall Equipment Efficiency in Manufacturing Industry

Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

A review of TinyML

In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of the Internet of Things (IoT) and edge computing. To estimate an outcome, traditional machine learning demands vast amounts of resources. The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware centered on deploying deep neural network models on embedded (micro-controller-driven) systems. TinyML will pave the way for novel edge-level services and applications that survive on distributed edge inferring and independent decision-making rather than server computation. In this paper, we explore TinyML's methodology, how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

A viability plan of a unit of research in applications of new telecommunications technologies

This project is about to develop a plan to create a dedicated unit in order to monitoring of emerging technologies in the field of telecommunications

Concept study of a Digital Twin of a Precision Agricultural Robot

When designing a digital twin, different properties are needed to be implemented so that the physical twin can be able to interact with the environment and fulfil the tasks that the physical asset was developed for. The methodology proposed in this thesis is of highly relevance when designing a digital twin solution, being simple to adapt to different necessities and with a clear architecture to utilize or to adjust to digital assets in different applications. The digital twin developed in the case study, on which this thesis is based, is the foundation of the development and creation of an innovative table grape harvesting robot. The main objective of this research is to review and identify potential methodologies that can be used in the design stage of a digital twin and to validate how the processes in the methodologies can support the system to fulfill the objectives of the project. The system involves the interactions between the robot, the environment, and the agronomical tasks that the robot needs to perform. This thesis creates the methodologies that will assist different stakeholders in easily identifying the processes that streamline the testing procedure of different algorithms in the digital twin, saving time and resources by doing the development in the digital twin and not in the physical object. The thesis assessed the challenges of limited testing time and transporting equipment and personnel difficulties to a fixed location, in this case, a vineyard located in Italy, defined later as the physical asset. It is of highly importance to incorporate the research structure to the digital twin development team early in the project's timeline. Based on the literature and discussion between stakeholders, the basic architecture was created, and from there, the cases defined in this thesis will allow the users and clients to test in a seamless way their products in the digital twin. The process gave the option to the users to select and use from a basic environment to a more complex and challenging one. The purpose of the thesis was to present and document certain architecture and methodologies used in the research and present them as a base for future developments in the area. This method can be used for projects when physical assets need to be created and tested, when time periods for testing are part of the challenges of the project, and the availability to allocate and integrate resources is complex. The main results and conclusion of this thesis is the methodology proposed, on how a simple processes and methodologies can be easily adapted to the necessities of any digital twin solution, and how the architecture proposed can have the ability to modify different cases for specific objectives. And finally, how it is possible to use, prepper and export the information needed to train the Machine learning (ML) algorithms, and to add noise specific to allow the evolution of the algorithms. The methodology proposed in this thesis can increase the quality and usability of any digital twin by proving how it can be successfully implemented during the planning developing process of a project. Furthermore, the methodology demonstrate that it can be easily adapted to the necessities of any digital twin solution and streamlined the progress in the future use of digital twins in any area. In the case study, the methodology helped all different stakeholders to utilize the digital twin to develop, test, and improve different algorithms from different locations through Europe without the need to build the physical robot, or being in one particular place, and without the restrictions of seasonal harvesting periods

Digital twins’ applications for building energy efficiency: a review

Over the last few decades, energy efficiency has received increasing attention from the Architecture, Engineering, Construction and Operation (AECO) industry. Digital Twins have the potential to advance the Operation and Maintenance (O&M) phase in different application fields. With the increasing industry interest, there is a need to review the current status of research developments in Digital Twins for building energy efficiency. This paper aims to provide a comprehensive review of the applications of digital twins for building energy efficiency, analyze research trends and identify research gaps and potential future research directions. In this review, Sustainability and Energy and Buildings are among the most frequently cited sources of publications. Literature reviewed was classified into four different topics: topic 1. Optimization design; topic 2. Occupants’ comfort; topic 3. Building operation and maintenance; and topic 4. Energy consumption simulation.This research was funded by the Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), grant number 22/2551-0000574-8.Peer ReviewedPostprint (published version