arXiv subjects

Abdollah Baghaei Daemei

Publications and source records attributed to Abdollah Baghaei Daemei.

3 recordsLinked to original sources

arXiv2026

Green-Roof Energy Performance in New Zealand's Present and Future Climate Condition (2050)

Green roofs are increasingly promoted as nature-based measures for reducing building energy demand, yet their performance in New Zealand's oceanic climates and under future weather remains insufficiently quantified. This condensed study compares an extensive green roof with a conventional bare roof on a standardized single-storey dwelling in Auckland, Christchurch, and Wellington. Dynamic annual simulations were conducted in DesignBuilder/EnergyPlus using present-day EnergyPlus Weather files and 2050 weather files generated with CCWorldWeatherGen. All non-roof building parameters were held constant so that differences in total fuel consumption (TFC) for heating and cooling could be attributed to the roof system. Under present weather, annual TFC decreased by approximately 3.1% in Auckland, 2.3% in Christchurch, and 1.5% in Wellington. Under 2050 weather, the corresponding reductions were 3.3%, 2.6%, and 1.1%. Summer benefits were larger in Auckland and Christchurch, reaching about 9.0% and 5.6%, respectively, in 2050, but remained marginal in Wellington. The findings show that green roofs can provide modest annual energy savings in oceanic climates, with stronger value as a summer heat-mitigation measure in warmer locations. Performance is strongly climate-dependent and should not be generalized without local simulation or field validation.

physics.ao-ph↗

arXiv2026

Prototyping an AI-powered Tool for Energy Efficiency in New Zealand Homes

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 for homeowners is fragmented. This study presents the design and evaluation of an AI-powered decision-support tool for residential energy efficiency in New Zealand. The prototype, developed using Python and Streamlit, integrates data ingestion, anomaly detection, baseline modeling, and scenario simulation (e.g., LED retrofits, insulation upgrades) into a modular dashboard. Fifteen domain experts, including building scientists, consultants, and policy practitioners, tested the tool through semi-structured interviews. Results show strong usability (M = 4.3), high value of scenario outputs (M = 4.5), and positive perceptions of its potential to complement subsidy programs and regulatory frameworks. The tool demonstrates how AI can translate national policies into personalized, household-level guidance, bridging the gap between funding, standards, and practical decision-making. Its significance lies in offering a replicable framework for reducing energy hardship, improving health outcomes, and supporting climate goals. Future development should focus on carbon metrics, tariff modeling, integration with national datasets, and longitudinal trials to assess real-world adoption.

cs.CY↗

arXiv2026

A Prototypical Decision-Support Tool for Household Energy Management: A New Zealand Case Study

This paper presents the system architecture and operating logic of The Home-Energy Check-Up (New Zealand), a web-based public decision-support prototype designed to help New Zealand households identify avoidable energy-cost leakage, complete a short guided home inspection, and generate a prioritized behavior-first energy roadmap. The application is implemented as a single-file Python Streamlit system with session-state navigation, a household input dataclass, conservative low-high saving estimators, a seven-check inspection layer, a recommendation-ranking layer, visual analytics, anonymous Google Sheets persistence, downloadable reports, and a certificate-of-completion interface. The system does not claim to be a certified energy audit, New Zealand Building Code H1 verification method, Healthy Homes compliance statement, or guaranteed bill-forecasting engine. Instead, it operationalizes a practical educational workflow: start with money, collect only the minimum required household profile, convert user answers into a score and action set, estimate annual savings using transparent formulas, and convert behavior savings into a staged save-to-upgrade pathway. The manuscript details the front-end, state-management, calculation, data-storage, visualization, recommendation, deployment, privacy, and limitation layers of the prototype. It also identifies research-grade improvements required before the tool is used for validated impact assessment, including external validation against measured energy data, robust concurrent data writes, clearer uncertainty calibration, accessibility testing, and formal user evaluation. The contribution is a reproducible architecture for translating household energy advice into an interactive, gamified, data-light decision-support pathway for New Zealand homes.

cs.HC↗