Recent years have seen a significant increase in the use of artificial intelligence (AI) and machine learning (ML) concepts across a variety of academic domains. AI's major objective is to build intelligent systems and give machines human intelligence. Artificial intelligence is a tool for creating systems, making judgements, solving problems, learning, and linguistic intelligence, as well as for imitating human conduct. Electrical and computer engineers are at the forefront of intellectual creativity as they participate in the planning, creation, evaluation, and production processes for newer generations of gadgets and technology. Even if these professionals want to grow, their objectives might conflict with the consequences of artificial intelligence, which are continually expanding. The practice of Artificial Intelligence (AI) and Machine Learning (ML) applications in industrial industries that have a significant influence on sustainability and the environment, such as renewable energy, smart grids, the catalyzed industry, and power storage and distribution The main popular approaches are artificial neural networks and Machine learning. Demand for energy is skyrocketing at a higher pace than production in the industry between 2004 and 2017, implying a decline in energy efficiency (EE). Under the premise of steady future output, an explicit energy efficiency enhancement target of 26% from 2017 and 2050 is set.
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The main techniques of AI are automation and robotics, Natural Language Processing, machine vision and machine learning. Some examples of Artificial Intelligence algorithms are classification, clustering, regression, and ensemble learning algorithms. Applications of Artificial Intelligence (AI) incorporates Healthcare, the auto industry, robotics, agriculture, e-commerce, education, data security, and social media, among other industries. The energy sector is facing challenges from heightened competition, productivity, a lack of statistics necessary for effective management, and altering supply and demand trends. Artificial intelligence is incorporated into renewable energy technology such as resource prediction, energy efficiency, and energy affordability.
(ONU World Population Prospects, 2019) by the end of this era, there will be more than 10.9 billion people on the planet, up from 7.7 billion in 2019 and 9.7 billion in 2050. Consumption for water, energy, and natural resources will rise in tandem with a spike in people on the planet, overtaxing biological systems and causing nature to gradually deteriorate because of increased energy use. This will have a broad impact on the built landscape. As a means of reducing greenhouse gas emissions, limiting global warming, and reducing the carbon footprint, ecological and efficient energy use is becoming more and more prevalent. The attempts of several administrations, businesses, and stakeholders demonstrate the widespread concern for effective energy management and energy efficiency as a means of reducing climate change and promoting environmental preservation. India's Nationally Determined Contributions (NDCs) were centred on Zero Effect, Zero Defect (ZED) manufacturing to improve energy and resource efficiency, to decrease pollution and handle waste, and to utilise renewable energy sources against the backdrop of the global efforts. The Indian government has reduced subsidies and raised taxes on fossil fuels by about 26% as a step towards the ZED aim, converting the carbon subsidy regime into a carbon taxation system. This section's main goal is to shed light on the direction that actual research into energy recommender systems is taking as well as the problems that are driving significant R&D in the near and distant future. The increasing success of machine learning (ML) approaches in handling routine categorization or prediction tasks has significantly increased the number of applications that use ML models, often implementing them as “black boxes” that are challenging for end users to comprehend. The shift of existing modern AI applications to modern explainable AI models is thought to depend on an ML model's capacity to “explain itself and its actions” to the consumers.
A component of artificial intelligence called machine learning uses many data kinds to complete tasks. A machine learning algorithm first gains knowledge from user data before solving prediction problems on its own. Machine learning approaches can be divided into three basic categories: supervised, unsupervised, and reinforcement learning. techniques use different mechanism to solves problem make use of machine learning. Applications of Machine learning includes self- driving cars, email spam and malware filtering, traffic prediction, image, and speech recognition etc (Pradhan B. et al, 2022).
Machine learning and natural linguistic interpretation have made an impact on practically every business and field of academic experiments, including engineering. Artificial intelligence (AI) is used by specialists in machine learning and electrical engineering to develop and improve technologies as well as provide the most recent data for AI to analyse. While the existing environment is conducive for the advancement of energy efficient techniques, the industry still lacks information, expertise, and experience in how to put these principles into practice. The efficient AI algorithms for various energy efficient applications includes Deep learning and Artificial Neural Networks.