arXiv:2509.05364v2 Announce Type: replace-cross Abstract: Residential buildings contribute significantly to energy use, health outcomes, and carbon emissions. In New Zealand, housing quality has historically been poor, with inadequate insulation and inefficient heating contributing to widespread energy hardship. Recent reforms, including the Warmer Kiwi Homes program, Healthy Homes Standards, and H1 Building Code upgrades, have delivered health and comfort improvements, yet challenges persist. Many retrofits remain partial, data on household performance are limited, and decision-making support
The increasing focus on climate change mitigation and energy efficiency, coupled with advancements in AI and data analytics, makes this a timely development for addressing residential energy use.
This prototype demonstrates how AI can be deployed to solve practical, local-level problems with significant national implications for energy consumption, public health, and climate goals.
The availability of AI tools to optimize energy efficiency in residential buildings could accelerate retrofitting efforts and provide data-driven support for policy decisions.
- · AI developers
- · Energy efficiency consultants
- · New Zealand residents
- · Government climate initiatives
- · Traditional energy suppliers (long term)
- · Inefficient building material manufacturers
Increased adoption of AI tools for residential energy management leads to measurable reductions in household energy consumption.
Improved energy efficiency in homes could shift energy demand curves, impacting grid planning and investment in new generation capacity.
Successful localized AI applications in energy efficiency could become a blueprint for broader AI integration into urban planning and infrastructure management globally.
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Read at arXiv cs.AI