{"help": "https://data.gov.au/data/api/3/action/help_show?name=package_show", "success": true, "result": {"archived": false, "author": "", "author_email": null, "contact_point": "unknown", "creator_user_id": "c2fbbe4a-4ba0-4945-808b-67454605a4cf", "duplicate_score": 2, "geospatial_topic": [], "id": "959046a7-aac8-4353-8487-9dfb0c281a91", "isopen": false, "license_id": "CC-BY-4.0", "license_title": "CC-BY-4.0", "maintainer": "", "maintainer_email": "", "metadata_created": "2025-11-15T11:59:17.341422", "metadata_modified": "2026-01-10T07:40:08.425664", "name": "solar_pv_potential_monthly_cumulative_kbf", "notes": "This dataset aggregates photovoltaic generation potential to monthly and annual totals for each building, providing the energy budget metrics most directly applicable to economic feasibility analysis and renewable energy target setting.\n\nThe dataset delivers cumulative PV output (kWh) for 12 months plus annual totals across approximately 16,000 buildings, incorporating panel performance characteristics and temperature efficiency adjustments. These values represent the total energy that could be generated under standard mid-performance panel specifications, accounting for maximum feasible panel placement on each rooftop.\n\nMonthly cumulation serves economic and policy applications: calculating annual revenue under feed-in tariffs, estimating return on investment timelines, assessing progress toward municipal renewable energy goals, and comparing seasonal capacity factors across building types. The dataset enables rapid screening of high-potential properties and portfolio-level solar capacity assessment.\n\nKey users include energy consultants conducting pre-feasibility studies, local government officers tracking climate action plan progress, and commercial property managers evaluating solar investment across multiple buildings. Financial analysts can use annual totals for cash flow projections, while urban planners can aggregate values to understand district or city-wide renewable generation potential.\n\nValues assume optimal panel specifications and placement. For custom panel types, users should apply efficiency ratios to the irradiance datasets. For sub-monthly temporal analysis (grid integration, demand matching), reference the hourly dataset.\n\nComprehensive technical documentation, including data dictionary, usage examples, and processing methodology, is available in <a href=\"https://resources.aurin.org.au/ckan/resources/DataSet-Information.pdf\">here</a>; software stack details and calculation workflows are documented in <a href=\"https://resources.aurin.org.au/ckan/resources/Software_Stack.pdf\">here</a>.", "num_resources": 1, "num_tags": 5, "organization": {"id": "b0429d6f-82d8-4c5f-9325-37b64b9b1c48", "name": "school-of-electrical-engineering-computing-and-mathematical-sciences-curtin-university-australian-ur", "title": "School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University", "type": "organization", "description": "Our school brings together Curtin\u2019s core capabilities across electrical engineering, computing, physics and mathematics, to better understand and advance our data-driven world. We have built substantial knowledge in machine learning, cyber security, statistics and optimisation, digital transformation, radio astronomy, the Internet of Things, signal processing, embedded systems, renewable and power engineering. The school offers a range of undergraduate and postgraduate courses across these areas, which give students the skills they need to tackle the challenges of the future. The school attracts millions of dollars of research income each year, the majority of which is from industry, reflecting the focus of the school on demand driven research. 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