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.

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.

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.

This paper describes a simulation to establish the extent to which reliance on non-dispatchable energy sources, particularly wind generation, could in the future be extended beyond accepted norms, by utilizing the distributed battery capacity of an electric vehicle fleet for storage. The notion of exploiting the distributed battery capacity of an electric vehicle fleet as grid storage is not new. However, this simulation study specifically examines the potential impact of the idea in the New Zealand context. The simulation makes use of real and projected data in relation to vehicle usage, full potential wind generation capacity and availability, taking into account weather variation, and typical daily and seasonal patterns of electricity usage. It differs from previous studies in that it is based on individual vehicles, rather than a bulk battery model. At this stage, the simulation does not take into account local or regional flows. A more detailed analysis of these localized effects will follow in subsequent stages of the simulation work.