Machine learning reduces soft costs for residential solar photovoltaics

Dong, Changgui; Nemet, Gregory F.; Gao, Xin; Barbose, Galen; Sigrin, Ben; O’Shaughnessy, Eric

doi:10.1038/s41598-023-33014-4

Cited by 5 publications

(3 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 73 These electronic devices convert sunlight into electrical energy through the photovoltaic effect, generating electricity when sunlight interacts with the cell’s materials and causes the expulsion of electrons. 74 The basic building block of a solar cell is a p-n junction, which allows for the generation of electricity when sunlight interacts with the cell’s materials. 75 Solar cell technology has evolved to utilize p-n junctions in photovoltaic solar cells, which consist of p- and n-type semiconductors coupled with a p-n junction to produce electric current.…”

Section: Solar Cellmentioning

confidence: 99%

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Dhahi,

Yousif Dafhalla,

Tayfour

et al. 2024

iScience

View full text Add to dashboard Cite

Section: Solar Cellmentioning

confidence: 99%

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Dhahi,

Yousif Dafhalla,

Tayfour

et al. 2024

iScience

View full text Add to dashboard Cite

“…Teng Zhong et al [29] showed the potential of city-scale rooftops, which reduce the labor cost through deep learning with the proposed spatial optimization sampling strategy. Dong's [30] research showed that machine learning can reduce customer acquisition costs by 15% (USD 0.07/Watt), and they identified new market opportunities for solar companies to expand and diversify their customer bases. Soft costs and customer acquisition costs are notoriously difficult to reduce due to the challenges in predicting forthcoming events.…”

Section: Economic Analysis and Impacts On Societymentioning

confidence: 99%

Business Perspectives of Distributed System Operators for Solar Rooftop-as-a-Service

Leewiraphan,

Ketjoy,

Thanarak

2023

Energies

View full text Add to dashboard Cite

Rising fossil energy prices and the significantly decreasing prices of energy technology have resulted in electricity consumers having the option to install solar PV rooftops to rely on the self-consumption of clean energy. However, the increase in this amount is affecting the revenue of electricity as a utility, which must adapt and develop its business model to accommodate the situation. If the utility cannot be adapted in time, it may lead to a loss of income from services and the sale of electricity from fossil energy. The utility in Thailand’s electricity market that acts as the distribution system operator (DSO) is known as the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA) is responsible for managing distribution networks and customers. There are four types of solar rooftop-as-a-service (RaaS) business perspectives they could consider as opportunities through which to minimize revenue impact. The business services were designed for the DSO customer as follows: Consulting, Design, and Installation (CDI); Operation and Maintenance (O&M); Energy Service Company (ESCO); and Power Purchase Agreement (PPA). The model comprises four customer segments: residential buildings and small-, medium-, and large-scale commercial buildings. This paper applies SWOT, Five Forces, 4P marketing, and economic impact analyses to identify the possibilities when using the DSO business model. The SWOT analysis demonstrates that ESCO and PPA are strengths in the DSO’s performance characteristics and existing customer data. In the electricity industry, both models offer enormous customer bargaining power in terms of a Five Forces analysis. The main reason is that there is currently high competition in the installation service. In the 4P analysis result, the price per unit is found to be significantly lower than in residential scenarios. Therefore, there is a format for presenting promotions with an advantage over competitors. Deploying an after-sales service that brings convenience to all customer segments is needed. The economic analysis conducted using Cournot competition game theory shows a significant differential in the Medium (M) and Large (L) customer sectors’ competition due to lower technology prices. In conclusion, with the current regulatory framework and criteria, the ESCO and PPA show the best practical model from a utility business perspective. The recommendation for DSO is to create a strategic ecosystem and to link it with private companies as their partnership business.

show abstract

“…As more consumers select rooftops for PV installation, companies require data and tools to predict solar irradiation [7]. However, accurately predicting solar irradiation becomes crucial with the growing use of rooftop PV installations [8]. By 2030, building-integrated PV in EU member states is projected to surpass 22% of Europe's annual power consumption [1,9].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach for 3D Solar Potential Assessment at the City Scale

Waqas,

Jiang,

Shang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site’s context, a mean annual solar estimate of 99.97 kWh/m2/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m2/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m2/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions.

show abstract

Machine learning reduces soft costs for residential solar photovoltaics

Cited by 5 publications

References 62 publications

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Business Perspectives of Distributed System Operators for Solar Rooftop-as-a-Service

An Integrated Approach for 3D Solar Potential Assessment at the City Scale

Contact Info

Product

Resources

About