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

DMLA: A Dynamic Model-Based Lambda Architecture for Learning and Recognition of Features in Big Data

Title from PDF of title page, viewed April 19, 2017Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 57-58)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2016Real-time event modeling and recognition is one of the major research areas that is yet to reach its fullest potential. In the exploration of a system to fit in the tremendous challenges posed by data growth, several big data ecosystems have evolved. Big Data Ecosystems are currently dealing with various architectural models, each one aimed to solve a real-time problem with ease. There is an increasing demand for building a dynamic architecture using the powers of real-time and computational intelligence under a single workflow to effectively handle fast-changing business environments. To the best of our knowledge, there is no attempt at supporting a distributed machine-learning paradigm by separating learning and recognition tasks using Big Data Ecosystems. The focus of our study is to design a distributed machine learning model by evaluating the various machine-learning algorithms for event detection learning and predictive analysis with different features in audio domains. We propose an integrated architectural model, called DMLA, to handle real-time problems that can enhance the richness in the information level and at the same time reduce the overhead of dealing with diverse architectural constraints. The DMLA architecture is the variant of a Lambda Architecture that combines the power of Apache Spark, Apache Storm (Heron), and Apache Kafka to handle massive amounts of data using both streaming and batch processing techniques. The primary dimension of this study is to demonstrate how DMLA recognizes real-time, real-world events (e.g., fire alarm alerts, babies needing immediate attention, etc.) that would require a quick response by the users. Detection of contextual information and utilizing the appropriate model dynamically has been distributed among the components of the DMLA architecture. In the DMLA framework, a dynamic predictive model, learned from the training data in Spark, is loaded from the context information into a Storm topology to recognize/predict the possible events. The event-based context aware solution was designed for real-time, real-world events. The Spark based learning had the highest accuracy of over 80% among several machine-learning models and the Storm topology model achieved a recognition rate of 75% in the best performance. We verify the effectiveness of the proposed architecture is effective in real-time event-based recognition in audio domains.Introduction -- Background and related work -- Proposed framework -- Results and evaluation -- Conclusion and future wor

A Framework to Develop Anomaly Detection/Fault Isolation Architecture Using System Engineering Principles

For critical systems, timely recognition of an anomalous condition immediately starts the evaluation process. For complex systems, isolating the fault to a component or subsystem results in corrective action sooner so that undesired consequences may be minimized. There are many unique anomaly detection and fault isolation capabilities available with innovative techniques to quickly discover an issue and identify the underlying problems. This research develops a framework to aid in the selection of appropriate anomaly detection and fault isolation technology to augment a given system. To optimize this process, the framework employs a model based systems engineering approach. Specifically, a SysML model is generated that enables a system-level evaluation of alternative detection and isolation techniques, and subsequently identifies the preferable application(s) from these technologies A case study is conducted on a cryogenic liquid hydrogen system that was used to fuel the Space Shuttles at the Kennedy Space Center, Florida (and will be used to fuel the next generation Space Launch System rocket). This system is operated remotely and supports time-critical and highly hazardous operations making it a good candidate to augment with this technology. As the process depicted by the framework down-selects to potential applications for consideration, these too are tested in their ability to achieve required goals

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Improving the earthquake resilience of primary schools in the border regions of neighbouring countries

This work summarises the strategy adopted in the European research project PERSISTAH. It aims to increase the resilience of the population, focusing on the existing primary schools in the Algarve (Portugal) and Huelva (Spain) regions. Software was developed to assess the seismic safety of these schools, considering different earthquake scenarios. Seismic retrofitting measures were studied and numerically tested. Some of them were also implemented in the retrofitting activities of two case study schools (one in each country). It was found that the adopted ground motion prediction equations (GMPEs) considerably affect the results obtained with the software, especially for offshore earthquake scenarios. Furthermore, the results show that the masonry buildings would be the most damaged school typologies for all the scenarios considered. Additionally, a set of guidelines was created to support the school community and the technicians related to the construction industry. The goal of these documents is to increase the seismic resilience of the population. Different activities were carried out to train schoolteachers in seismic safety based on the guidelines produced, obtaining positive feedback from them.info:eu-repo/semantics/publishedVersio