2022
DOI: 10.1155/2022/1541938
|View full text |Cite
|
Sign up to set email alerts
|

Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning

Abstract: Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Bo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 7 publications
(3 citation statements)
references
References 20 publications
0
3
0
Order By: Relevance
“…The work of Mukilan et al [39] develop a forecast of the solar potential through photovoltaic panels from the roofs using the restricted Boltzmann machine as a machine learning method. The simulation results show that the proposed method achieves a higher rate of forecast accuracy than other compared methods.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…The work of Mukilan et al [39] develop a forecast of the solar potential through photovoltaic panels from the roofs using the restricted Boltzmann machine as a machine learning method. The simulation results show that the proposed method achieves a higher rate of forecast accuracy than other compared methods.…”
Section: Related Workmentioning
confidence: 99%
“…Some investigations propose methodologies to analyze the performance of photovoltaic modules and the efficiency of energy conversion with organic photovoltaic cells ( [23,24]). In addition, works were found that combine metaheuristic techniques with machine learning techniques [25], seek to reduce energy consumption based on accurate forecasts of photovoltaic energy requirements using 5G technology [32], and develop forecasts of solar potential through rooftop photovoltaic panels using the constrained Boltzmann machine [39].…”
Section: Related Workmentioning
confidence: 99%
“…With enhanced prediction performance on households’ PV adoption behavior, our research could be used to lower customer acquisition costs and identify new market opportunities for PV installation companies. Many studies in the literature have applied machine learning methods to estimate solar potential 39 , 40 and deployment density 41 43 , detect solar panels in aerial images 44 46 , and to forecast PV power generation 47 49 . Two recent studies also used machine learning to classify PV adopters and non-adopters; however, they either had very limited data on PV adopters (e.g., 30 adopters) 50 or their prediction performance could be further improved 51 .…”
Section: Introductionmentioning
confidence: 99%