Investigation of Solar Energy for Urban Areas and Metropolitan Communities Using Machine Learning

Abstract

This study explores the assessment of solar panels in urban and community settings through the application of machine learning algorithms, specifically Artificial Neural Networks (ANNs) and Support Vector Machine-Long Short-Term Memory (SVM-LSTM). The objective of this evaluation is to determine the extent to which solar technology benefits these communities. Linear regression is a valuable tool for data analysis when there is a clear linear relationship between inputs and outputs. However, when dealing with non-linear datasets, more sophisticated analysis methods are required. Support Vector Machines (SVM), a machine learning technique rooted in statistical learning theory, offer an effective approach for addressing non-linear regression challenges. The study’s central focus lies in leveraging machine learning techniques to evaluate the viability of solar panels in urban and community contexts. This evaluation utilizes two distinct algorithms, ANN and SVM-LSTM, to assess the impact of solar technology on these communities. By employing advanced machine learning methodologies, the study aims to shed light on whether solar energy solutions hold the potential to significantly benefit urban and community environments. The results of this analysis may have far-reaching implications for sustainable energy adoption in urban areas, paving the way for a more environmentally responsible and energy-efficient future