Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance

Multi-objective decision analytics for short-notice bushfire evacuation: An Australian case study

This paper develops a multi-objective optimisation model to compute resource allocation,shelter assignment and routing options to evacuate late evacuees from affected areas to shelters.Three bushfire scenarios are analysed to incorporate constraints of restricted time-window and potential road disruptions.Capacity and number of rescue vehicles and shelters are other constraints that are identical in all scenarios.The proposed mathematical model is solved by ?-constraint approach.Objective functions are simultaneously optimised to maximise the total number of evacuees and assigned rescue vehicles and shelters.We argue that this model provides a scenario-based decision-making platform to aid minimise resource utilisation and maximise coverage of late evacuees

Mild-to-moderate schizotypal traits relate to physiological arousal from social stress

Schizotypy denotes psychosis-like experiences, such as perceptual aberration, magical ideation and social anxiety. Altered physiological arousal from social stress is found in people with high schizotypal traits. Two experiments aimed to determine the relationship of schizotypy to physiological arousal from social stress. Experiment 1 tested the hypotheses that heart rate from social stress would be greater in high, than mild-to-moderate, schizotypal traits, and disorganised schizotypy would explain this effect. Experiment 1 tested social stress in 16 participants with high schizotypal traits and 10 participants with mild-to-moderate schizotypal traits. The social stress test consisted of a public speech and an informal discussion. The high schizotypal group had higher heart rate than the mild-to-moderate schizotypal group during the informal discussion, but not during the public speech. Disorganised schizotypy accounted for this group difference. Experiment 2 tested the hypothesis that mild-to-moderate schizotypal traits would have a linear relationship with physiological arousal from social stress. Experiment 2 tested 24 participants with mild-to-moderate schizotypal traits performing the abovementioned social stress test while their heart rate and skin conductance responses were measured. Mild-to-moderate schizotypal traits had a linear relationship with physiological arousal during the discussion with a stranger. Distress in disorganised schizotypy may explain the heightened arousal from close social interaction in high schizotypy than mild-to-moderate schizotypy. Mild-to-moderate schizotypal traits may have a linear relationship with HR during close social interaction because of difficulty with acclimatising to the social interaction

Self-building Artificial Intelligence and machine learning to empower big data analytics in smart cities

YesThe emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.Supported by the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program and a La Trobe University Postgraduate Research Scholarship

Evaluating Sustainable Options for Valorization of Rice By-Products in Sri Lanka: An Approach for a Circular Business Model

Due to the significant quantities of waste generated by the Sri Lankan rice industry, circular bioeconomy methodologies were applied to examine value-adding entrepreneurial activities for rice industry by-products (RIB). The study was conceived after scouring the existing literature on agricultural waste management and interviewing experts in the field and the rice industry. In the first phase, the suitability of valorizing alternatives for RIB was considered via a multi-criteria decision-making method. Valorization options, such as biochar production, energy purposes, composting, and other activities, were evaluated using an analytical hierarchy process (AHP) based on four criteria, namely environmental, social, technical, and economic issues. The results indicated that the highest priority should be given to environmental, social, and economic considerations, with local priority vectors of 0.5887, 0.2552, and 0.0955, respectively. It was found that biochar production is the optimal valorization strategy for managing RIB in Sri Lanka. From these findings, the development of a sustainable business model for making biochar out of RIB was done based on commercial motivations and value addition in biochar manufacturing processes. The Business Model Canvas elements played a vital role in categorizing and interpreting the case study data. Though the RIB seems undervalued at present, it was found that as a direct result of environmental concerns, several stakeholders have developed RIB valorization with an emphasis on bioenergy generation and biochar production. Adequate subsidies (technology and knowledge), standard regulations, more collective actions for creating economies of scale, and marketing strategies (consumer awareness) are all necessary for the successful implementation of sustainable circular business models

Perceptions of Australians with diabetes-related foot disease on requirements for effective secondary prevention

Introduction Secondary prevention is essential in reducing recurrence of diabetes-related foot disease (DFD) but is frequently poorly implemented in clinical practice. Objective To explore the perceptions of people with diabetes-related foot disease (DFD) on their self-perceived knowledge in managing DFD, facilitators and barriers influencing their DFD care, and ideas and preferences for a secondary prevention program. Design Sixteen people with a history of DFD from Queensland and Victoria, Australia, underwent semi-structured interviews. Interviews were audio-recorded over telephone and transcribed and analysed following a thematic framework. Participants were asked about their experiences and perceptions relating to DFD and factors influencing the care they receive for DFD relevant to the development of a secondary prevention program for DFD. Findings and discussion Participants had high self-perceived knowledge in managing DFD, especially in implementing healthy lifestyle changes and conducting daily foot checks and foot care, though most received support from family members acting as carers. However, issues with access and adherence to offloading footwear, and a lack of clear education received on footwear and other aspects of DFD care were perceived as major barriers. Improved patient education, provided in a consistent manner by proactive clinicians was perceived as an essential part of secondary prevention. Telehealth was perceived positively through facilitating faster care and considered a good adjunct to standard care. Health and technological literacy were considered potentially major barriers to the effectiveness of remote care. Conclusion People with DFD require improved access to offloading footwear and education about secondary prevention, which could be provided by telehealth with adequate support

Advanced analytics for harnessing the power of smart meter big data

Smart meters or advanced metering infrastructure (AMI) are being deployed in many countries around the world. Smart meters are the basic building block of the smart grid and governments have invested vast amounts in smart meter deployment targeting wide economic, social and environmental benefits. The key functionality of the smart meter is the capture and transfer of data relating to the consumption (electricity, gas) and events such as power quality and meter status. Such capability has also resulted in the generation of an unprecedented data volume, speed of collection and complexity, which has resulted in the so called big data challenge. To realize the hidden value and power in such data, it is important to use the appropriate tools and technology which are currently being called advanced analytics. In this paper we define a smart metering landscape and discuss different technologies available for harnessing the smart meter captured data. Main limitations and challenges with existing techniques with big data are also highlighted and several future directions in smart metering are presented

Smart electricity meter data intelligence for future energy systems: A survey

Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments