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

Optimal fleet replacement: A case study on a Spanish urban transport fleet

[EN] Optimizing the average annual cost of a bus fleet has become an increasing concern in transport companies management around the world. Nowadays, there are many tools available to assist managerial decisions, and one of the most used is the cost analysis of the life cycle of an asset, known as ``life cycle cost¿¿. Characterized by performing deterministic analysis of the situation, it allows the administration to evaluate the process of fleet replacement but is limited by not contemplating certain intrinsic variations related to vehicles and for disregarding variables related to exigencies of fleet use. The main purpose of this study is to develop a combined model of support to asset management based in the association of the life cycle cost tool and the mathematical model of Monte Carlo simulation, by performing a stochastic analysis considering both age and average annual mileage for optimum vehicle replacement. The utilized method was applied in a Spanish urban transport fleet, and the results indicate that the use of the stochastic model was more effective than the use of the deterministic model.De Sa-Riechi, JL.; Macian Martinez, V.; Tormos, B.; Avila, C. (2017). Optimal fleet replacement: A case study on a Spanish urban transport fleet. Journal of the Operational Research Society. 68(8):886-894. doi:10.1057/s41274-017-0236-1S886894688Avila da S., C. R., & Beck, A. T. (2015). New method for efficient Monte Carlo–Neumann solution of linear stochastic systems. Probabilistic Engineering Mechanics, 40, 90-96. doi:10.1016/j.probengmech.2015.02.006Boudart, J., & Figliozzi, M. (2012). Key Variables Affecting Decisions of Bus Replacement Age and Total Costs. Transportation Research Record: Journal of the Transportation Research Board, 2274(1), 109-113. doi:10.3141/2274-12Collan, M., & Liu, S. (2003). Fuzzy logic and intelligent agents: towards the next step of capital budgeting decision support. Industrial Management & Data Systems, 103(6), 410-422. doi:10.1108/02635570310479981Erol, I., & Ferrell, W. G. (2003). A methodology for selection problems with multiple, conflicting objectives and both qualitative and quantitative criteria. International Journal of Production Economics, 86(3), 187-199. doi:10.1016/s0925-5273(03)00049-5Feng, W., & Figliozzi, M. (2013). An economic and technological analysis of the key factors affecting the competitiveness of electric commercial vehicles: A case study from the USA market. Transportation Research Part C: Emerging Technologies, 26, 135-145. doi:10.1016/j.trc.2012.06.007Khasnabis, S., Alsaidi, E., & Ellis, R. D. (2002). Optimal Allocation of Resources To Meet Transit Fleet Requirements. Journal of Transportation Engineering, 128(6), 509-518. doi:10.1061/(asce)0733-947x(2002)128:6(509)Keles, P., & Hartman, J. C. (2004). CASE STUDY: BUS FLEET REPLACEMENT. The Engineering Economist, 49(3), 253-278. doi:10.1080/0013791049049895

Human performance prediction in man-machine systems. Volume 1 - A technical review

Tests and test techniques for human performance prediction in man-machine systems task

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Efficient preliminary floating offshore wind turbine design and testing methodologies and application to a concrete spar design

The current key challenge in the floating offshore wind turbine industry and research is on designing economic floating systems that can compete with fixed-bottom offshore turbines in terms of levelized cost of energy. The preliminary platform design, as well as early experimental design assessments, are critical elements in the overall design process. In this contribution, a brief review of current floating offshore wind turbine platform pre-design and scaled testing methodologies is provided, with a focus on their ability to accommodate the coupled dynamic behaviour of floating offshore wind systems. The exemplary design and testing methodology for a monolithic concrete spar platform as performed within the European KIC AFOSP project is presented. Results from the experimental tests compared to numerical simulations are presented and analysed and show very good agreement for relevant basic dynamic platform properties. Extreme and fatigue loads and cost analysis of the AFOSP system confirm the viability of the presented design process. In summary, the exemplary application of the reduced design and testing methodology for AFOSP confirms that it represents a viable procedure during pre-design of floating offshore wind turbine platforms.Peer ReviewedPostprint (author’s final draft