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

Controller performance design and assessment using nonlinear generalized minimum variance benchmark : scalar case

A nonlinear version of the Generalized Minimum Variance (GMV) multivariable control law has been recently derived for the control of nonlinear, possibly time-varying systems. This paper presents the results of the controller performance assessment against this Nonlinear GMV controller in the scalar case. The minimum variance of the generalized output is estimated from routine operating data given only the plant time delay and the technique is applied to a nonlinear reactor control example

Quantum Model Averaging

Standard tomographic analyses ignore model uncertainty. It is assumed that a given model generated the data and the task is to estimate the quantum state, or a subset of parameters within that model. Here we apply a model averaging technique to mitigate the risk of overconfident estimates of model parameters in two examples: (1) selecting the rank of the state in tomography and (2) selecting the model for the fidelity decay curve in randomized benchmarking.Comment: For a summary, see http://i.imgur.com/nMJxANo.pn

Digital zero noise extrapolation for quantum error mitigation

Zero-noise extrapolation (ZNE) is an increasingly popular technique for mitigating errors in noisy quantum computations without using additional quantum resources. We review the fundamentals of ZNE and propose several improvements to noise scaling and extrapolation, the two key components in the technique. We introduce unitary folding and parameterized noise scaling. These are digital noise scaling frameworks, i.e. one can apply them using only gate-level access common to most quantum instruction sets. We also study different extrapolation methods, including a new adaptive protocol that uses a statistical inference framework. Benchmarks of our techniques show error reductions of 18X to 24X over non-mitigated circuits and demonstrate ZNE effectiveness at larger qubit numbers than have been tested previously. In addition to presenting new results, this work is a self-contained introduction to the practical use of ZNE by quantum programmers.Comment: 11 pages, 7 figure

Benchmarking and incentive regulation of quality of service: an application to the UK electricity distribution utilities

Quality of service has emerged as an important issue in post-reform regulation of electricity distribution networks. Regulators have employed partial incentive schemes to promote cost saving, investment efficiency, and service quality. This paper presents a quality-incorporated benchmarking study of the electricity distribution utilities in the UK between 1991/92 and 1998/99. We calculate technical efficiency of the utilities using Data Envelopment Analysis technique and productivity change over time using quality-incorporated Malmquist indices. We find that cost efficient firms do not necessarily exhibit high service quality and that efficiency scores of cost-only models do not show high correlation with those of quality-based models. The results also show that improvements in service quality have made a significant contribution to the sector�s total productivity change. In addition, we show that integrating quality of service in regulatory benchmarking is preferable to cost-only approaches

Performance assessment of MIMO systems under partial information

Minimum variance (MV) can characterize the most fundamental performance limitation of a system, owing to the existence of time-delays/infinite zeros. It has been widely used as a benchmark to assess the regulatory performance of control loops. For a SISO system, this benchmark can be estimated given the information of the system time delay. In order to compute the MIMO MV benchmark, the interactor matrix associated with the plant may be needed. However, the computation of the interactor matrix requires the knowledge of Markov parameter matrices of the plant, which is rather demanding for assessment purposes only. In this paper, we propose an upper bound of the MIMO MV benchmark which can be computed with the knowledge of the interactor matrix order. If the time delays between the inputs and outputs are known, a lower bound of the MIMO MV benchmark can also be determined