title: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption

authors: Pasandideh, Seyedmostafa

abstract: In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

text: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

year: 2025

title: An appraisal of nutrient supplies available for tree growth in a pumice soil

authors: Knight, P.J.

abstract: Chemical analyses have confirmed and explained the results of an earlier pot trial in which the availability of major nutrients in six pumice ash layers of Kaingaroa silty sand was assessed by the growth of radiata pine seedlings.
Almost all of the tree-available P is found in the present topsoil: the quantities-of P that occur in two buried soils (Waimihia and Rotoma ashes) are almost entirely in the form of organic P which is apparently very resistant to breakdown due to complexing with allophane. The N in these layers is similarly unavailable.
The mineral layers, about 4 ft in thickness (Taupo pumice and lapilli), which lie between the present topsoil and the uppermost buried soil, are very low in total N and P and exchangeable Mg, but relatively high in exchangeable K. Only the lower buried soil contains a reasonable quantity of exchangeable Mg and has a Mg : K ratio in favour of Mg.

text: An appraisal of nutrient supplies available for tree growth in a pumice soil Chemical analyses have confirmed and explained the results of an earlier pot trial in which the availability of major nutrients in six pumice ash layers of Kaingaroa silty sand was assessed by the growth of radiata pine seedlings.
Almost all of the tree-available P is found in the present topsoil: the quantities-of P that occur in two buried soils (Waimihia and Rotoma ashes) are almost entirely in the form of organic P which is apparently very resistant to breakdown due to complexing with allophane. The N in these layers is similarly unavailable.
The mineral layers, about 4 ft in thickness (Taupo pumice and lapilli), which lie between the present topsoil and the uppermost buried soil, are very low in total N and P and exchangeable Mg, but relatively high in exchangeable K. Only the lower buried soil contains a reasonable quantity of exchangeable Mg and has a Mg : K ratio in favour of Mg.

year: 1970

title: He ara whakaora i whaia e atahi wahine i tukinohia = Long term effects of childhood sexual abuse among Maori women: A healing path for abused women

authors: Hippolite Wright, Debbie

subjects: Sexual abuse victims

abstract: He Ara Whakaora I Whaia E Etahi Wahine I Tukinohia: A Healing Path for Abused Women is a qualitative study of long term effects of Childhood Sexual Abuse among Maori women. Thirteen Maori women of diverse ages, sexual orientations, socioeconomic, religious, and tribal backgrounds shared their life experiences in a series of semistructured interviews. The women describe the physical, social and emotional impact the sexual abuse had on them. lndepth case studies are used to explore how these women rallied their personal, interpersonal, and social resources to survive the abuse, and ultimately find a way of healing. A theoretical synthesis comprising Tikanga Maori, Ecological Psychology, Social Systems theory, and Feminist theory is used to shed light on the participants' narratives. They also identify characteristics of their perpetrators, most were male and all but one was a trusted individual. A culturally competent model of healing for Maori survivors of sexual abuse was generated from the collective survival and healing strategies described by the participants.

text: He ara whakaora i whaia e atahi wahine i tukinohia = Long term effects of childhood sexual abuse among Maori women: A healing path for abused women He Ara Whakaora I Whaia E Etahi Wahine I Tukinohia: A Healing Path for Abused Women is a qualitative study of long term effects of Childhood Sexual Abuse among Maori women. Thirteen Maori women of diverse ages, sexual orientations, socioeconomic, religious, and tribal backgrounds shared their life experiences in a series of semistructured interviews. The women describe the physical, social and emotional impact the sexual abuse had on them. lndepth case studies are used to explore how these women rallied their personal, interpersonal, and social resources to survive the abuse, and ultimately find a way of healing. A theoretical synthesis comprising Tikanga Maori, Ecological Psychology, Social Systems theory, and Feminist theory is used to shed light on the participants' narratives. They also identify characteristics of their perpetrators, most were male and all but one was a trusted individual. A culturally competent model of healing for Maori survivors of sexual abuse was generated from the collective survival and healing strategies described by the participants.

year: 1998