Machine learning has revolutionized industry practices by addressing complex operational challenges. Overheating server temperature due to administrative negligence at Institut Teknologi Sepuluh Nopember’s Directorate of Technology and Information Systems Development (DPTSI) is a problem that can be solved with the help of machine learning. This research aims to create a user-friendly tool that enables DPTSI’s server administrators to predict and prevent server-related problems effectively, thereby reducing downtime. The results of this study were obtained by the combination of iTransformer as a prediction model, Random Forest as a classification model, and Mistral-7B as a generative AI model deployed on a web-based application used to help server administrator decision-making abilities by predicting future server conditions and providing solutions on how to resolve predicted problems. This research contributes to the advancement of machine learning applications in industrial society, offering a practical solution for server management through innovative model combinations and implementation strategies.

In this paper, we introduce a macro-micro model for predicting taxi demands. Our model is a composite deep learning model that integrates multiple views Our network design specifically incorporates the spatial and temporal dependency of taxi or ride-hailing demand, unlike previous papers that also utilize deep learning models. In addition, we propose a hybrid of Long Short-Term Memory Networks and Temporal Convolutional Networks that incorporates real-world time series with long sequences. Finally, we introduce a microscopic component that attempts to extract insights revealed by roaming vacant taxis. In our study, we demonstrate that our approach is competitive against a large array of approaches from the literature on the basis of detailed moving logs of more than 20,000 taxis and 12 million trips per month over a three-month period. Our analysis of the effectiveness of individual components reveals that microscopic information is essential for generating high-quality predictions.

Machine perception of emotions is integral to the development of human-centric Artificial Intelligence (AI) in sustainable industrial applications. Human expressions of emotions are not always direct. Word embeddings are mature techniques that can extract the semantics of such indirect expressions from text data. However, they are not primed to extract emotions. In this paper, we propose a novel approach that generates robust word embeddings for implicit and explicit expressions of emotion. This approach consists of two techniques, mask and rogue, we evaluate both techniques on two benchmark datasets for emotion classification. Our results confirm the effectiveness of the proposed approach in extracting emotions from diverse contexts. We have shared the emotion word embedding for public use.

Contemporary energy platforms are leveraging advanced data management and Artificial Intelligence (AI) capabilities in response to the increasing complexity of energy systems and grids. Despite these advances, it is a non-trivial and challenging task to support the decision-making needs of the human operators of such complex energy-related implementations. Conversational agents or chatbots are a potential emerging technology that can be utilized to address this challenge. Although there is a large body of literature on chatbots in general, they are not robust as they rely on predefined conversational pathways that are inadequate to efficiently address the complexities of dynamic data spaces in energy platforms. The capability of generating answers in real-time by communicating with the dynamic dataspace is crucial as energy management decisions are real-time and time sensitive. In this paper, we present the design and development of Cooee, a chatbot for conversational engagement with the dynamic data spaces of complex energy environments. Cooee leverages state-of-art language models along with rule-based language processing methods for a conversational interaction with dynamic data spaces, which consequently supports and enables decision-making by human experts. We have developed Cooee as a standalone application and then integrated into a real-world energy AI platform deployed within a multi-campus tertiary education institution setting. Cooee was empirically evaluated in this setting and compared with several state-of-the-art Q&A approaches.

Microgrids and energy platforms have become increasingly intricate in nature. This is especially true in platforms adopted in large multi functional complexes which are spread across geographies of varying climactic conditions. Many of these complexes have evolved over time as organisations have grown and diversified, this has resulted in a mix of infrastructure, technology, networks, systems and equipment woven in an inextricable web. Often managing such complex hierarchies involve interacting with heterogeneous management platforms from various vendors and built on different platforms each providing the solution to a piece of the ’energy puzzle’ of the organisation. The complexities involved in managing such a network of systems are manifold and require extensive resourcing and expertise. Sustainability and Net Zero carbon emission initiatives that aim to achieve international and national targets add a further layer of complexity to the task at hand. In this paper, we propose a Cloud Edge architecture that leverages Artificial Intelligence (AI) and data analytics for microgrid energy optimisation and net zero carbon emissions. This architecture provides an intelligent and cohesive abstraction to assist in cataloging, unifying and managing the complexities of microgrids and enabling sustainable management of energy. The proposed architecture has been operationalised as the energy management and optimisation platform at a multi-campus, multi-functional tertiary education institution. Empirical evaluations conducted on this deployment have generated results that confirm the function and effectiveness of this architecture in addressing the emerging and evolving challenges of microgrid energy optimisation and net zero carbon emissions.

Safe and effective vaccination is leading the recovery from the COVID-19 pandemic. Despite the urgency of full vaccination that prevents serious illness, a state of vaccine messaging augmented by disinformation campaigns in online social networks has emerged. Several studies have established a link between social media activity and vaccine messaging, and most platforms are actively removing vaccine disinformation. The objective of this study is to apply a validated Artificial Intelligence (AI) framework to extract, analyze and synthesize themes, emotions and emotion transitions associated with COVID-19 vaccine messaging in online social networks. We applied the framework on approximately 400,000 COVID-19 vaccine-related posts and conversations on two social media platforms, Twitter and Reddit, from March 2020 to September 2021. The results of this study are threefold, firstly, the discovery of a minority of implied anti-vaccine themes on infertility, microchips, gene editing and fetal cells that have remained undetected. Secondly, the discovery of six themes that capture a majority of the vaccine messaging namely, social lockdown measures, frontline healthcare providers, side effects, vaccine distribution, breakthrough infections and vaccine efficacy on variants. Thirdly, the variety and intensity of emotions expressed since the start of the pandemic, and comparatively negative emotions being expressed in recent months. We anticipate the findings of our study will contribute towards improved vaccine messaging as the world returns to a new normal from the COVID-19 pandemic.

Time series forecasting is a critical requirement for the optimal operation of energy grids, systems, and platforms, where the forecasting challenge itself can span across energy consumption, renewables generation, and energy utilisation. Artificial Intelligence (AI) algorithms and models have been leveraged to predict these time series forecasts with increasing levels of accuracy. In contrast to local models that are developed separately for each time series, Global Models, which are trained across many sets of time series drawing on characteristics of ’relatedness’, have produced more accurate forecasts. In this paper, we propose a transformer architecture based global model as a generalist forecaster of energy time series data, where we frame a sequence forecasting model and represent numerical values of the corresponding time series as vector embeddings in this model. We evaluate this transformer architecture based global model on real-world time-series energy data generated by the La Trobe Energy AI platform (LEAP), a functional and operational microgrid deployed in the multicampus tertiary education setting of La Trobe University, Australia. The results of these experiments confirm that the proposed generalist forecasting approach outperforms specialist local models trained on individual time series. We also demonstrate the ability of this approach to forecast dissimilar time series from the same model.