A predictive model of users’ behavior and values of smart energy meters using PLS-SEM

© Springer Nature Switzerland AG 2020. A smart energy metering system is an IoT device that connects several electrical household devices and record, monitor, estimate, control in-house energy consumption in a real-time basis. Although smart energy meters have great capabilities, this technology is still in infancy stages in many developing countries, and little is known about what perceived values are associated with smart meters from residents’ perspectives. Therefore, this research aimed to fill this gap by examining the impact of six different types of perceived values on residents’ intentions to use smart meters in UAE. The study followed a quantitative approach by gathering 266 survey responses which were tested by using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The statistical results genuinely indicated that perceived epistemic values, environmental values, emotional values, and convenience values can significantly impact residents’ intention to use smart meter, whereas social values and monetary values found to have no significant impact on their intentions to use this technology. Theoretical and practical implications are indicated, and directions of future research are specified afterwards

XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks

Forecasting is an important task for intelligent agents involved in dynamical processes. A specific application domain concerns district heating networks, in which the future heating load generated by centralized power plants and distributed to buildings must be optimized for better plant maintenance, energy consumption and environmental impact. In this paper we present XM_HeatForecast a Python tool designed to support district heating network operators. The tool provides an integrated architecture for i) generating and updating in real-time predictive models of heating load, ii) supporting the analysis of prediction performance and errors, iii) inspecting model parameters and analyzing the historical dataset from which models are trained. A case study is presented in which the software is used on a synthetic dataset of heat loads and weather forecast from which a regression model is generated and updated every 24 h, while predictions of load in the next 48 h are performed every hour. Software available at: https://github.com/XModeling Video available at: https://youtu.be/JtInizI4e_s

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Computational intelligence on short-term load forecasting : a methodological overview

201904 bcmaVersion of RecordPublishe

A novel detection algorithm to identify false data injection attacks on power system state estimation

201908 bcmaVersion of RecordPublishe

The nanotechnology of life-inspired systems

For some decades now, nanotechnology has been touted as the 'next big thing' with potential impact comparable to the steam, electricity or Internet revolutions - but has it lived up to these expectations? While advances in top-down nanolithography, now reaching 10-nm resolution, have resulted in devices that are rapidly approaching mass production, attempts to produce nanoscale devices using bottom-up approaches have met with only limited success. We have been inundated with nanoparticles of almost any shape, material and composition, but their societal impact has been far from revolutionary, with growing concerns over their toxicity. Despite nebulous hopes that making hierarchical nanomaterials will lead to new, emergent properties, no breakthrough applications seem imminent. In this Perspective, we argue that the time is ripe to look beyond individual nano-objects and their static assemblies, and instead focus on systems comprising different types of 'nanoparts' interacting and/or communicating with one another to perform desired functions. Such systems are interesting for a variety of reasons: they can act autonomously without external electrical or optical connections, can be dynamic and reconfigurable, and can act as 'nanomachines' by directing the flow of mass, energy or information. In thinking how this systems nanoscience approach could be implemented to design useful - as opposed to toy-model - nanosystems, our choice of applications and our nanoengineering should be inspired by living matterclos