Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison
Botswana International University of Science and Technology · Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology · +2 more institutions
Abstract
The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML)…
Citation impact
- FWCI
- 31.13
- Percentile
- 100%
- References
- 84
Authors
6- JGJwaone GaboitaolelweCorresponding
Botswana International University of Science and Technology
- AMAdamu Murtala Zungeru
Botswana International University of Science and Technology
- AYAbid Yahya
Botswana International University of Science and Technology
- CKCaspar K. Lebekwe
Botswana International University of Science and Technology
- DNDasari Naga Vinod
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Topics & keywords
- Photovoltaic system
- Renewable energy
- Computer science
- Solar power
- Solar energy
- Grid parity
- Reliability (semiconductor)
- Reliability engineering
- Affordable and clean energy