AI Approach Towards Optimal Finding of Renewable Sources of Energy and Their Classification

Introduction

The integration of advanced artificial intelligence (AI) into Smart Energy systems necessitates a comprehensive understanding of computational, economic, and societal implications. Establishing this socio-technical platform requires defining the domain and specifying research problems cohesively. Sustainable development challenges post- industrial society, urging academia, industry, and society to seek tangible solutions. The allure of renewable energy (RE) emerged during the 1970s energy crisis when the risk of depleting conventional fuels became apparent, prompting the development of RE and resource conservation. Subsequent concerns about pollution, global warming, and resource scarcity in the 1980s highlighted the imperative to mitigate environmental damage. Integrating renewable into the energy sector significantly mitigates greenhouse gas emissions and import reliance, emphasizing the imperative of environmental protection. AI’s evolution permeates various sectors, influencing global productivity, equality, environmental outcomes, and more. Machine learning(ML) enables machines to learn from data, enhancing performance and predicting outputs autonomously. Note worthy ML algorithms in photovoltaic (PV) applications include support vector machines (SVM), neural networks (NNs), decision trees (DT), and deep learning (DL) techniques like convolution neural networks (CNNs), long-short-term memory (LSTM), and generative adversarial networks (GANs). DL, a subset of ML, eliminates manual feature extraction, allowing algorithms to autonomously extract features from raw data, marking a novel advancement in neural networks.

In addition to supervised learning, recent advancements have been introduced unsupervised ML approaches. In unsupervised methods, the computer algorithm autonomously identifies patterns in the data, operating in an “unsupervised” manner without user intervention. The primary application of unsupervised ML involves automatically categorizing data into distinct groups or “clusters” that exhibit similar characteristics. Unsupervised learning algorithm avoids the training with labeled and classified datasets. The time of training procedure is very optimal in unsupervised learning algorithm.

Within the realm of environmental studies, recent applications of unsupervised ML, such as t-distributed stochastic neighbor embedding (t-SNE) or k-means clustering, have been employed to categorize the carbon–fluorine bond dissociation energies of per- and polyfluoroalkyl substances (PFAS). These applications aim to comprehend bond dissociation energies, enabling the visualization of high-dimensional data as two-dimensional “clusters” where data points within a cluster share comparable characteristics. It is crucial to emphasize that these clusters are automatically identified by unsupervised ML algorithms, devoid of human intervention. Consequently, these results underscore the utility of such algorithms in automatically classifying and elucidating chemical trends in environmental contaminants, which would be challenging to detect manually.