title: Assessing the spatial dependence of welfare estimates obtained from discrete choice experiments

authors: Campbell, Danny

subjects: discrete choice experiments

abstract: We report results from a discrete choice experiment designed to assess the general public’s preferences regarding rural landscape improvements. Using a random parameters logit model to account for unobserved taste heterogeneity, we exploit the panel nature of the dataset to retrieve partworths, or willingness to pay (WTP) values, for every individual in the sample. We subsequently use the Moran’s I test statistic to gauge the spatial dependence of these estimates for a range of spatial extents. We find that WTP estimates are positively spatially autocorrelated, but the degree of homogeneity diminishes as the spatial extent becomes larger before it eventually becomes relatively stable.

text: Assessing the spatial dependence of welfare estimates obtained from discrete choice experiments We report results from a discrete choice experiment designed to assess the general public’s preferences regarding rural landscape improvements. Using a random parameters logit model to account for unobserved taste heterogeneity, we exploit the panel nature of the dataset to retrieve partworths, or willingness to pay (WTP) values, for every individual in the sample. We subsequently use the Moran’s I test statistic to gauge the spatial dependence of these estimates for a range of spatial extents. We find that WTP estimates are positively spatially autocorrelated, but the degree of homogeneity diminishes as the spatial extent becomes larger before it eventually becomes relatively stable.

year: 2008

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: Evaluation of land in Wairoa District for potential horticultural development

authors: Rau, Johnathon Taukiri

subjects: land suitability

abstract: The Wairoa District, on the East Coast of the North Island of New Zealand has less horticultural development than areas to the north and south. The objective of this study was to provide information to help inform landowners who may wish to invest in horticulture which could improve the economic situation of the district.
Field work involving local scale climate mapping and soil characterisation of areas with potential for horticulture in Wairoa District was completed in 2017. Constructed climate and topographical maps with existing soil maps were evaluated against known crop growth requirements to produce crop potential maps identifying areas with potential for crop production. Horticultural crops included in this study include kiwifruit, apples, cherries. Alternative crops include hemp and poppies.
Between mid-April – 31 October 2017, 45 portable iButton temperature loggers were deployed throughout Land Use Capability classes 1-3 in the Wairoa District and were set to record hourly temperature. When regressed against nearby climate stations, long term (18 -26 years) temperature datasets were derived from the short term iButton datasets. MODIS satellite images were also analysed to help identify areas prone to frost. From the long-term datasets local scale chill hour, growing degree days and October frost risk maps were constructed for the Wairoa District. For the central area from Wairoa to Frasertown, chill hours were estimated to range between 600 – 900, growing degree days 1300 – 1400 and October frost risk less than 25%.
From four representative soil types, horizon samples were taken to determine the soils physical properties including each horizons Total Available Water Holding Capacity (TAWHC). Horizon TAWHC for each soil type were summed to give TAWHC to 1 meter which along with 21 years of estimated daily rainfall and potential evapotranspiration were used in a soil water balance model to estimate seasonal crop irrigation requirements. Irrigation estimates for kiwifruit compared well against published values ranging between mean 204 – 247 mm.
The crop potential maps with seasonal crop irrigation estimates can enhance a land owner’s ability to make informed decisions resulting in economic benefit to whanau, community and the Wairoa District.

text: Evaluation of land in Wairoa District for potential horticultural development The Wairoa District, on the East Coast of the North Island of New Zealand has less horticultural development than areas to the north and south. The objective of this study was to provide information to help inform landowners who may wish to invest in horticulture which could improve the economic situation of the district.
Field work involving local scale climate mapping and soil characterisation of areas with potential for horticulture in Wairoa District was completed in 2017. Constructed climate and topographical maps with existing soil maps were evaluated against known crop growth requirements to produce crop potential maps identifying areas with potential for crop production. Horticultural crops included in this study include kiwifruit, apples, cherries. Alternative crops include hemp and poppies.
Between mid-April – 31 October 2017, 45 portable iButton temperature loggers were deployed throughout Land Use Capability classes 1-3 in the Wairoa District and were set to record hourly temperature. When regressed against nearby climate stations, long term (18 -26 years) temperature datasets were derived from the short term iButton datasets. MODIS satellite images were also analysed to help identify areas prone to frost. From the long-term datasets local scale chill hour, growing degree days and October frost risk maps were constructed for the Wairoa District. For the central area from Wairoa to Frasertown, chill hours were estimated to range between 600 – 900, growing degree days 1300 – 1400 and October frost risk less than 25%.
From four representative soil types, horizon samples were taken to determine the soils physical properties including each horizons Total Available Water Holding Capacity (TAWHC). Horizon TAWHC for each soil type were summed to give TAWHC to 1 meter which along with 21 years of estimated daily rainfall and potential evapotranspiration were used in a soil water balance model to estimate seasonal crop irrigation requirements. Irrigation estimates for kiwifruit compared well against published values ranging between mean 204 – 247 mm.
The crop potential maps with seasonal crop irrigation estimates can enhance a land owner’s ability to make informed decisions resulting in economic benefit to whanau, community and the Wairoa District.

year: 2018