Spatial adoption forecast methodology for photovoltaic systems throughout a city

• Published in: Sustainable cities and society 2023-06, Vol.93 (C), p.104430, Article 104430
• Main Authors: Jones, C. Birk, Vining, William, Haines, Thad
• Format: Article
• Language: English
• Subjects: Adoptions; Data-driven; Forecast; Geospatial analysis; Integration; Permits; Photovoltaic; Zoning districts
• ISSN: 2210-6707; 2210-6715
• DOI: 10.1016/j.scs.2023.104430

This work predicts future adoptions of distributed photovoltaic (PV) systems throughout an entire city using open-source geographic information system (GIS) data. The approach combines census income and building zoning data into a single geographic district map where adoptions are likely to be consistent from year to year. Existing PV system locations for each year are input into a data-driven model for each of the combined income and building type districts to predict future PV installations. In this work, two algorithms were tested as part of the methodology: linear least-squares regression and the Bass Diffusion model. Using a linear regression algorithm, in this paper’s test city (Santa Fe, New Mexico, U.S.A), the percentage of loads with PV was predicted to increase from 5.2% in 2020 to 18% in 2050. In the same test city, the Bass Diffusion model predicted and increase in PV to be about 27% of the all the buildings by 2050. This simple but detailed approach provides electric utilities with a useful tool for planning assessments or municipalities can use the results to inform policy decisions. The approach differs from existing literature in that it offers a data-driven prediction methodology that is influenced by past trends and also consider local building types and economics. •Uses existing trends to predict future photovoltaic adoptions at the city level.•Compares a linear model with a Bass Diffusion model for adoption forecasting.•Combines historical data, economic factors, and building zoning to construct a detailed spatiotemporal forecast of photovoltaic adoption across a large area.