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Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning

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 Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.
- King Saud University Saudi Arabia
- Kyungpook National University
- SRM Institute of Science and Technology, Tamil Nadu India
- Kyungpook National University Korea (Republic of)
- Kalasalingam Academy of Research and Education India
Renewable energy, Artificial intelligence, Electricity Price and Load Forecasting Methods, TJ807-830, Geometry, Renewable energy sources, Engineering, Solar energy, Artificial Intelligence, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Machine Learning Methods for Solar Radiation Forecasting, Electrical and Electronic Engineering, Grid, Granularity, Photovoltaic system, Test data, Energy, Electricity Price Forecasting, Renewable Energy, Sustainability and the Environment, Statistics, Load Forecasting, Photovoltaic Maximum Power Point Tracking Techniques, Computer science, Programming language, Operating system, Electrical engineering, Computer Science, Physical Sciences, Energy (signal processing), Short-Term Forecasting, Probabilistic Forecasting, Mathematics, Forecasting
Renewable energy, Artificial intelligence, Electricity Price and Load Forecasting Methods, TJ807-830, Geometry, Renewable energy sources, Engineering, Solar energy, Artificial Intelligence, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Machine Learning Methods for Solar Radiation Forecasting, Electrical and Electronic Engineering, Grid, Granularity, Photovoltaic system, Test data, Energy, Electricity Price Forecasting, Renewable Energy, Sustainability and the Environment, Statistics, Load Forecasting, Photovoltaic Maximum Power Point Tracking Techniques, Computer science, Programming language, Operating system, Electrical engineering, Computer Science, Physical Sciences, Energy (signal processing), Short-Term Forecasting, Probabilistic Forecasting, Mathematics, Forecasting
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).7 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
