This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-scale systems. The proposed framework provides a unified environment for the visual representation and management of interconnected energy components, supporting informed decision-making among diverse stakeholder groups. The I-UCDT framework adopts a modular plug-and-play architecture based on the Functional Mock-up Interface (FMI) standard, enabling scalable and interoperable integration of heterogeneous energy models from platforms such as Modelica, MATLAB/Simulink, and EnergyPlus. A standardised data layer processes and structures raw model inputs, while an interactive visualisation layer translates complex energy flows into intuitive, user-accessible insights. By applying human–computer interaction principles, the framework reduces cognitive load and enables users with varying technical backgrounds to explore supply–demand balancing, decarbonisation pathways, and optimisation strategies. It supports the full lifecycle of energy system design, planning, and operation, offering flexibility for both industrial and community-scale applications. A case study demonstrates the framework’s potential to enhance transparency, usability, and energy efficiency. Overall, this work advances digital twin research for energy systems by combining technical interoperability with explicitly formalised user-centred design characteristics (C1–C10) to promote flexible and sustainable energy system management.

The integration of renewable energy into residential microgrids presents significant challenges due to solar generation intermittency and variability in household electricity demand. Traditional forecasting methods, reliant on historical data, fail to adapt effectively in dynamic scenarios, leading to inefficient energy management. This paper introduces a novel adaptive energy management framework that integrates streaming machine learning (SML) with a hierarchical fractal microgrid architecture to deliver precise real-time electricity demand forecasts for a residential community. Leveraging incremental learning capabilities, the proposed model continuously updates, achieving robust predictive performance with mean absolute errors (MAE) across individual households and the community of less than 10% of typical hourly consumption values. Three battery-sizing scenarios are analytically evaluated: centralised battery, uniformly distributed batteries, and a hybrid model of uniformly distributed batteries plus an optimised central battery. Predictive adaptive management significantly reduced cumulative grid usage compared to traditional methods, with a 20% reduction in energy deficit events, and optimised battery cycling frequency extending battery lifecycle. Furthermore, the adaptive framework conceptually aligns with digital twin methodologies, facilitating real-time operational adjustments. The findings provide critical insights into sustainable, decentralised microgrid management, emphasising improved operational efficiency, enhanced battery longevity, reduced grid dependence, and robust renewable energy utilisation.

With growing environmental concerns and the urgent need to mitigate global warming, there is a significant push towards adopting renewable energy sources, such as solar and wind power, which are crucial for reducing reliance on fossil fuels. However, the inherent variability of these sources presents substantial challenges for effective energy management, especially in the industrial sector. This research, focusing on the meat processing industry, adopts a twopronged approach to tackle these issues. It starts by using a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) to analyze open-source weather data, aiming to predict the impact of weather variations on renewable energy production. This predictive effort is crucial for enhancing the reliability of renewable sources in industrial applications. Despite the advancements in forecasting, the variable nature of energy supply necessitates efficient management strategies. Therefore, the study implements a Fuzzy Logic system to manage electricity consumption based on real-time energy availability and demand within the meat processing industry. Chosen for its robustness in handling uncertainty, Fuzzy Logic enables more informed decision-making under ambiguous conditions, thereby reducing reliance on conventional energy grids and improving energy use efficiency. This dual strategy aims to foster more sustainable and environmentally friendly industrial operations, addressing both the variability of renewable energy sources and the challenges in energy consumption management.

There is a pressing need to expand electricity production in Aotearoa New Zealand to meet sustainability goals and lower energy costs. This new generation needs to be based on renewable sources, chiefly wind and solar, for both sustainability and economic reasons. While there remains a role for the legacy grid, microgrids provide a means of co-locating generation with load, minimising transmission line investment and energy losses. This paper explores the advantages of smart community microgrids in this context, but also examines the challenges in terms of the existing legacy grid approach. Three case studies are given as examples, covering an isolated community with no grid connection, a more conventional residential community of 30 households, and a community with local commercial/industrial loads in addition to housing. These case studies show the benefits in terms of local consumption of locally generated electricity coupled with sharing or local trading within the community. Microgrids can support New Zealand's transition to a more electrified, equitable, economical and low-emissions energy system, but their development does require not just exploitation of new technologies, but also adjustment to the legacy grid model and a fresh approach to electricity infrastructure planning and management.

Digital Twins (DTs) are high-fidelity virtual models that behave-like, look-like and connect-to a physical system. In this work, the physical systems are operations and processes from energy-intensive industrial plants and their local communities. The creation of DTs demands expertise not just in engineering, but also in computer science, data science, and artificial intelligence. Here, we introduce the Adaptive Digital Twins (ADT) concept, anchored in five attributes inspired by the self-adaptive systems field from software engineering. These attributes are self-learning, self-optimizing, self-evolving, self-monitoring, and self-protection. This new approach merges cutting-edge computing with pragmatic engineering needs. ADTs can enhance decision-making in both the design phase and real-time operation of industrial facilities and allow for versatile 'what-if' scenario simulations. Seven applications within the energy-intensive industries are described where ADTs could be transformative.

This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.

As global COVID-19 pandemic response has moved from full lockdowns and partial lockdowns in most parts of the world to a post-COVID era, an interesting new phenomenon that has emerged is the increased prevalence of hybrid meetings with a mixture of online and in-person attendees. The opportunity for remote participants to observe the responses and interactions of others in the meeting is generally accepted as being limited. An experimental prototype system, called Wedge Video, has been constructed as an attempt to improve the experience of remote participants in hybrid in-person/remote meetings. Wedge Video uses standard screen and camera equipment with existing video conferencing software (Zoom). An evaluation of the prototype system was conducted based on three simple games that each required players to interact rapidly and with some use of body language or gaze direction. Encouraging results led to the examination of the geometry of screen and camera placement in detail. A system that has a somewhat 'virtual reality' feeling to it has now been developed. The remote user is given a view of the in-person part of the meeting with participants at the same scale and location as they would be if the remote user were at the table themselves. Similarly, the local participants see the remote person in place at their table, at a realistic scale and with close to accurate gaze direction. A very preliminary evaluation of these concepts has been promising.

Analysts are often interested in understanding the association between variables within a dataset. This paper describes a set of techniques for augmenting the Heatmap Matrix, which represents pairwise intersections of categorical variables. The proposed extensions include adapting the design and layout of the matrix to enhance its readability, expanding the number of metrics that can be presented, displaying matching records in a coordinated table view, and embedding the Chi-square test of independence. These features are demonstrated on two datasets using the empirical prototype that has been developed.

Because of cheap conversion technology, zero green-house-gas (GHG) emission, and abundant availability almost everywhere, solar energy is contended to be a cost-effective alternate source of energy. It has the potential to meet ever-increasing energy demands while mitigating the environmental concerns associated with fossil fuels. As solar energy is intermittent in nature, the design of a reliable and cost-effective system requires site-specific weather information. A real-time and low-cost portable solar power monitoring system is a realistic solution for the assessment of energy generation at any site. Real-time site-specific solar power generation data for existing weather conditions is also important for the cyber physical representation of a solar photovoltaic system known as digital twin. In these contexts, a simple real-time and low-cost solar power monitoring system is proposed, implemented, and tested in this paper. The system is designed using a low-cost edge computing device, a Raspberry Pi, a voltage sensor, a current sensor, and the measured data is monitored through the Thing Speak ® cloud platform. Test results of the system, which consists of two 5W photovoltaic modules, show its potential to be used in assessing site specific power generation capacity, PV plant performance monitoring, and in developing a digital twin of a PV system.

New Zealand is committed to decarbonize its energy sector by 2050, and from the perspectives of greenhouse gas emissions, and efficiency in generation, transmission and distribution, electricity is the energy source of choice. To meet the significant increasing demand for electricity, solar photovoltaic and wind turbine generators have the potential to offer cost-effective and environmentally friendly solutions. Solar photovoltaic (PV) and wind turbine generator (WTG) have the potential to offer a cost-effective and environmentally friendly solution. Energy storage systems (ESS) such as battery (BT), capacitor (CAP), pumped-hydro energy storage (PHES) and thermal energy storage (TES) are commonly integrated with these renewable energy sources (RESs) to overcome their intermittency and non-dispatchability. An optimal combination of renewable energy sources (RESs), ESSs, their operation and control along with load flexibility can enhance the cost-effectiveness and reliability of a system. The impact of on-site TES on total system cost and excess energy generation of a grid connected PV-WTG-ESS system for different renewable fraction (RF) is investigated. The electrical demand of a meat-factory and its edged community is considered in optimal sizing of the system. The demand of a meat-factory is mainly composed of its thermal load, electrical load and the charging of electric freights transports, whereas the community load used here comprises the household appliances of 3000 houses and their belonging electric vehicles. Half of both the EVs and freight transport are charged at night and the rest are charged during the day. The optimal sizing of different elements for given renewable fractions (RF) is performed using a Genetic Algorithm (GA). The result suggests that integration of TES provides enhanced flexibility to RESs and thus reduces the generation of excess energy and total cost of a HRES significantly. The sharing of on-site generation from RESs in a meat factory to its edged residential community improves cost-effectiveness and enhances the reliability of a HRES.

Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori-language users identified by the Indigenous Tweets website, pre-processing their data and filtering out non-Māori tweets, together with other sources of noise. Our motivation for creating such a resource is three-fold: (1) it serves as a rich and unique dataset for linguistic analysis of te reo Māori on social media; (2) it can be used as training data to develop and augment Natural Language Processing (NLP) tools with robust, real-world Māori-language applications; and (3) it will potentially promote awareness of, and encourage positive interaction with, the growing community of Māori tweeters, thereby increasing the use and visibility of te reo Māori in an online environment. While the corpus captures data from 2007 to 2020, our analysis shows that the number of tweets in the RMT Corpus peaked in 2014, and the number of active tweeters peaked in 2017, although at least 600 users were still active in 2020. To the best of our knowledge, the RMT Corpus is the largest publicly-available collection of social media data containing (almost) exclusively Māori text, making it a useful resource for language experts, NLP developers and Indigenous researchers alike.

Most large-scale language detection tools perform poorly at identifying M¯aori text. More-over, rule-based and machine learning-based techniques devised specifically for the M¯aori-English language pair struggle with interlingual homographs. We develop a hybrid architecture that couples M¯aori-language orthography with machine learning models in order to annotate mixed M ¯ aori-English text. This architecture is used to label a new bilingual Twitter corpus at both the token (word) and tweet (sentence) levels. We use the collected tweets to show that the hybrid approach outperforms existing systems with respect to language detection of interlin-gual homographs and overall accuracy. We also evaluate its performance on out-of-domain data. Two interactive visualisations are provided for exploring the Twitter corpus and comparing errors across the new and existing techniques. The architecture code and visualisations are available online, and the corpus is available on request.

Increasing use of online conferencing systems, particularly over the past year, has highlighted problems in these systems, especially their poor support for small group interactions within larger meetings. These include clumsy small group formation (e.g., issues around joining and leaving existing groups), the difficulty of getting the correct level of audio isolation between groups, poor provision for shared editing of documents, as well as fatiguing aspects of video conferencing caused by presentation format and the necessity of remaining on camera view. This paper describes the motivation, design and implementation of a prototype online conferencing system, called BubbleVideo. Building on both virtual world and pure video paradigms, it implements an extensive 2D world with shared documents, in which users appear through real-time video, presented in “bubbles” that can be moved around. Users are given the possibility of deciding whether to join a group by viewing a conversation “leakage”, which group members can share with outsiders.

Many design disciplines have made use of Virtual Reality (VR) technology within the design process but is the technology appropriate for adoption into communication design? Using an interview study with professional communication design practitioners, this paper presents insights on the perceived relevance of VR technology to the profession. Findings indicate that while participants saw potential utility in VR as a tool for communication design practice, it was not perceived as immediately suitable for professional use due to various factors which are identified and discussed.

Electric vehicles (EVs) are widely heralded as the silver bullet for greening personal transport. However their eventual impact in South Africa, a developing country with a low-capacity carbon-heavy grid, is questionable. This paper examines the potential impact of electrification of the vehicle fleet in South Africa, and explores the concept that large employers could take advantage of the country’s abundant sunshine and provide photovoltaic (PV) solar carports for employees to charge their vehicles while at work. We assess the extent to which this would reduce the potential burden on the national grid, and also consider the economic perspectives of the vehicle owners and the employers. Our assessment employs a mobility model and a battery model for the vehicles, and solar simulation with measured data for the PV generation. We show that without the provision of additional solar generation, charging four million vehicles from the grid would exceed the grid’s capacity. Further, the carbon footprint of an electric vehicle charged from the grid would be greater than that of a petrol-fuelled vehicle, negating any potential benefits of electrification. However, we demonstrate that photovoltaic charging at work renders electric vehicles more carbon-friendly than petrol equivalents, and has substantial financial benefits for the vehicle owner, the employer, and the grid.

Scheduled control of domestic electric water heaters, designed to cut energy use while minimising the impact on users' comfort and convenience, has been fairly common for some time in a number of countries. The aim is usually load-shifting (by heating water at off-peak times) and/or maximising time-of-use pricing benefits for users. The scheduling tends not to be linked to actual hot water usage and depends largely on stored thermal energy. Heat losses therefore tend to be greater than if the heater ran without a break. The effect of such a control strategy is thus to worsen the energy loss and in most cases increase greenhouse gas emissions. Many developing countries have flat-pricing (no time-of-use incentives) and rely heavily on energy from fossil fuels, making these considerations even more pressing. We explore three strategies for optimal control of domestic water heating that do not use thermostat control: matching the delivery temperature in the hot water, matching the energy delivered in the hot water, and a variation of the second strategy which provides for Legionella sterilisation. For each of these strategies we examine the energy used in heating, the energy delivered at the tank outlet, and issues of convenience to the user. The study differs from most previous work in that it uses real daily hot-water usage profiles, ensures like-for-like comparison in delivered energy at the point of use, and includes a daily Legionella avoidance strategy. We tackled this as an optimal control problem using dynamic programming. Our results demonstrate a median energy saving of between 8\ and 18% for the three strategies. Even more savings would be realised if intended and unintended usage events are correctly classified, and the optimal control only plans for intended usage events.

Whilst the primary purpose of conferences is work --- formal exchange and sharing of information --- they almost always also include elements of play: informal social and entertainment elements, such as receptions, dinners, and tourism activities. These activities also provide the opportunity for 'serious' discussions, meeting people, and networking, and are an essential part of a good conference. Despite this, most virtual conferencing tools fail to provide support for such activities, instead focusing on austere goals related to saving money, time, and travel. This paper describes the concept of a Virtual Cocktail Party (VCP) tool to integrate into a virtual conference environment. In VCP the 'party' is presented as a mixture of individuals and small conversation groups 'circulating' at the virtual venue. Exploiting an automated speech-to-text system, words from conversations are shown in word-clouds displayed around conversation groups, sufficient to identify topics of conversation allowing participants to decide whether or not to join a group.

The purpose of this study is to develop models for controlling electricity consumption with the goal of evolving efficiency in the electricity consumption system. The models developed for the electricity consumption problem attempts to investigate the consumption pattern of individual electric appliances in a building to allow for more efficient electricity consumption. The time-based electricity consumption visualizations for appliances used in this research study is carried out to evaluate the level of efficiency in electricity consumption. This paper presents a bottom-up modelling approach for stochastic electricity consumption data profiles in households. By collecting household electricity consumption data, a model is developed for domestic electricity consumption based on daily activity profiles for individual appliances. As a means of validating the model, a statistical comparison is made between measured data collected for appliances over a period in Hamilton, New Zealand and simulated data sets from these measurements. The output of the proposed domestic load model may be designed to meet specific requirements of consumers or integrated into other models.