Work

A web-based decision support tool for multifarious renewable energy systems

Year: 2025

Type: article

Abstract: Current global electricity demand is unprecedented which has perpetuated severe energy shortages and an increased reliance and use of unsustainable non-renewable sources, particularly in countries across Africa. In South Africa, the growing energy demand, coupled with aging coal-based electricity infrastructure has fuelled interest, discourses, and research on the potential use of renewable energy. Therefore, this study aimed to use Geographic Information Systems and Remote Sensing techniques to create a web-based interactive decision-making tool to measure and assess the availability and potential of wind, solar, and biomass energy sources, as well as to identify most suitable sites for wind farms in the Atlantis area of South Africa’s Western Cape. This was achieved by implementing a map mashup using JavaScript and Hypertext Mark-up Language (HTML) to retrieve information about wind, solar, and biomass potential together with suitable areas to locate wind farms. The maximum solar radiation received by rooftops was approximately 1499 kWh/m2, which could potentially generate 287.9 kilowatts of electricity using a solar panel efficiency of 15% and a performance ratio of 86%. Similarly, the urban wind energy potential via building-integrated wind turbines was 102.3 kilowatts, utilizing a nominal power of 10% and a minimum area of 24m2. Additionally, the biomass estimation was around 586.4 mg/ha, with the potential to generate 7.5 kilowatts of electricity using a conversion efficiency of 20% and a heating value of 4.5. Consequently, the web-based platform, provides a one-stop resource for investors, planners, and policymakers to access and make well-informed decisions about multifarious renewable energy potentials.

Source: Renewable energy focus

DOI: 10.1016/j.ref.2025.100702

Authors: Montseng Ramafikeng, Oluibukun Ajayi, Adedayo Adeleke

Institutions: University of Pretoria, Namibia University of Science and Technology

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $1,790