Prediction of building-scale solar energy potential in urban environment based on parametric modelling and machine learning algorithms
Bibliographic Data
| ID | 21234076 |
|---|---|
| Authors | Jia Tian (0000-0003-0677-6646, The University of Tokyo, corresponding author), Ryozo Ooka (0000-0002-9036-067X, The University of Tokyo) |
| Year | 2025 |
| Volume | 119 |
| Pages | 106057 |
| Publication date | 2025-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2024.106057 |
| OpenAlex | W4405364937 |
| Language | EN |
| Citations received | 7 |
| References cited | 44 |
Algorithm · Geography · Industrial engineering · Machine learning · Parametric statistics · Solar energy · Statistics · Building Energy and Comfort Optimization · Computer Science · Engineering · Environmental Science · Mathematics · Solar Radiation and Photovoltaics · Wind and Air Flow Studies · Artificial Intelligence
Scheduled adaptive recalibration for sustainable and accurate demand forecasting in urban building energy management
Unveiling regional disparities in China's photovoltaic development capability through a multidimensional assessment framework
Integrated physics-machine learning for real-time urban photovoltaic mapping
Unveiling cross-sector energy demand dynamics in Portugal
Life-cycle analysis of building-scale solar energy potential in urban contexts
An efficient 3D morphological analysis framework for pedestrian-level thermal stress assessment in megacities
Rooftop PV layout generation and optimization model for large-scale building cluster
Deep learning in neural networks
The random subspace method for constructing decision forests
A tutorial on support vector regression
Evaluation for block-scale solar energy potential of industrial block and optimization of application strategies
Evaluation of solar energy potential for residential buildings in urban environments based on a parametric approach
Understanding the relationship between urban morphology and solar potential in mixed-use neighborhoods using machine learning algorithms
Using intelligent multi-objective optimization and artificial neural networking to achieve maximum solar radiation with minimum volume in the archetype urban block
Crossing multiple solar energy gaps
| Citations per year | 7 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |