The University of Nottingham Ningbo China

About: The University of Nottingham Ningbo China is a education organization based out in Ningbo, Zhejiang, China. It is known for research contribution in the topics: Computer science & China. The organization has 7491 authors who have published 7153 publications receiving 83555 citations.

A review of data-driven approaches for prediction and classification of building energy consumption

Abstract: A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, including those methods for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those methods for classification (K-mean clustering, self-organizing map and hierarchy clustering). The review results demonstrate that the data-driven approaches have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the context of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies. It also paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.

Metamorphic Testing: A Review of Challenges and Opportunities

Abstract: Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this article, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.

Redox Electrolytes in Supercapacitors

Abstract: © The Author(s) 2015. Most methods for improving supercapacitor performance are based on developments of electrode materials to optimally exploit their storage mechanisms, namely electrical double layer capacitance and pseudocapacitance. In such cases, the electrolyte is supposed to be electrochemically as inert as possible so that a wide potential window can be achieved. Interestingly, in recent years, there has been a growing interest in the investigation of supercapacitors with an electrolyte that can offer redox activity. Such redox electrolytes have been shown to offer increased charge storage capacity, and possibly other benefits. There are however some confusions, for example, on the nature of contributions of the redox electrolyte to the increased storage capacity in comparison with pseudocapacitance, or by expression of the overall increased charge storage capacity as capacitance. This report intends to provide a brief but critical review on the pros and cons of the application of such redox electrolytes in supercapacitors, and to advocate development of the relevant research into a new electrochemical energy storage device in parallel with, but not the same as that of supercapacitors.