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 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.

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

“…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.…”

“…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].…”

Machine learning reduces soft costs for residential solar photovoltaics

“…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 .…”