AI-Powered IoT in the Energy Industry

The existence of sunlight, air, and different resources on Earth must be used wisely for human welfare while also safeguarding the environment and its living creatures. The use of the sunlight and air as a significant source of renewable energy (RE) is already an object of research and development in recent years. The high integration costs of various RE energy sources are a significant hurdle to their development. The RE contributes different energy domains: wind energy, solar energy, geothermal energy, hydro energy, ocean energy, bioenergy, hydrogen energy, and hybrid energy. Nowadays, artificial intelligence (AI) plays an inevitable role in RE, and it could assist in achieving the future goals of the RE. AI approaches in the research and development of the abovementioned RE sources will be comprehensively analyzed. This chapter analyzes and discusses challenges estimating the value creation of AI methods in RE.

Environmental change is probably the greatest danger that every human being is at present confronting. With these difficulties which lie before the public authority and different energy arrangement suppliers, it has become essential for them to give a supportable method of sustainable energy. Sustainable energy has been elective energy with petroleum products, as well. The significant benefit is that sustainable energy is a lot more secure, cleaner, and eco-friendly than existing resources. Artificial Intelligence (AI) and machine learning (ML) have become significant innovation technologies as the business is continually searching for approaches to be taken into account that quickly expand the interest for transparency, modesty, and reliability. These cutting-edge innovations can possibly investigate the past, upgrade the present, and foresee what’s to come. The machine learning approach and its applications are a subset of AI, where calculations figure out how to recognize designs from information with negligible human intervention. Many organizations use it to discover approaches to improve or foresee forthcoming changes that would influence their business. This example-based forecast can help sustainable power, as well. Since renewable energy depends on nature, their productivity and creation can fluctuate generally. Better prescient instruments can help energy organizations and clients take advantage of these establishments. This implies that AI and ML can possibly tackle the vast majority of the difficulties that currently exist. With innovation making quick headways, the environmentally friendly power area has gained huge headway somewhat recently. Notwithstanding, there are a couple of difficulties that actually win which can be tended to with the assistance of AI and ML. There is no question that the interest in solar energy is and will increment sooner rather than later which makes it more significant for the interest in arising advancements like AI, ML, and IoT to further develop efficiency and conquer the setbacks. Indeed, solar energy has significantly profited from the force of AI, AI-prescient models, and information science over the previous years. They have figured out how to bring down their expenses, improve expectations, and increment their portfolio’s pace of return.

Smart grids enable a two-way data-driven flow of electricity, allowing systematic communication along the distribution line. Smart grids utilize various power sources, automate the process of energy distribution and fault identification, facilitate better power usage, etc. Artificial Intelligence plays an important role in the management of power grids, making it even smarter. With the help of Artificial Intelligence and Internet of Things, smart grids can optimize the energy consumption, provide continuous feedback on usage, and monitor live usage statistics, thereby making the energy intelligent. Smart grids require specific hardware to continuously monitor and adapt to the requirements of the system. By enabling energy intelligence, we empower building-level and city-level optimizations that make use of green energy, thereby contributing more toward sustainable development. Thus, the multifaceted energy management system uses sustainable and renewable energy sources, combined with smart devices to provide a two-way communication system to optimize the end-to-end distribution of energy, beneficial to both suppliers and consumers.

Mobile technology is becoming more sophisticated as it advances. A comparison of different mobility scenarios was conducted. This chapter examined how electric vehicles interact with local energy systems in Stuttgart. Utilizing a travel demand model, a charging profile based on mobility patterns was generated for electric vehicles. During a quarter, charging demand and standard household load profiles were used to analyze peak hour load flow for 349 households. Considering that peak loads and charging capacity are usually separated in time, greater charging capacity might lead to lower utilization of transformers. Furthermore, a study was conducted to determine if the existing infrastructure was adequate for future demand, focusing on substation transformer reserves. Electromobility is a rapidly growing and evolving application domain of the Internet of Things, with a huge market potential in various areas. It incorporates many stakeholders – from manufacturers to the players of the energy market – with all sorts of physical and virtual resources. It is essential to allow these devices and systems to collaborate to create advanced e-mobility services.

The Internet of Things (IoT) is a network that connects computing devices, machines, nonliving objects, animals, and people provided with unique IDs. This allows transfer of data over the network without any human-to-human or human-to-computer interaction. The entities in such network may be a person, an animal with a biochip, a device with sensor, or any man-made thing or natural item with an Internet protocol. IoT is more helpful for applications like renewable energy, smart city planning, medical needs, security systems, etc. In renewable energy systems, it supports supply of energy, transfer of energy, energy distribution, and demand and helps to optimize the usage of energy and mitigates climate change. The involvement of IoT will improve energy efficiency and reduce the impact of environment in energy usage. This results in the optimized usage of renewable energy under proper monitoring. This chapter discusses on IoT toward smart grids and leveraging renewable energy.

Energy derived from natural sources, such as sunlight, wind, and water, is called green energy. Green energy is a source of energy derived from clean sources such as solar, wind, geothermal, and biomass. The environment benefits from green energy because green energy replaces the harmful effects of fossil fuels with more environmentally friendly options. Green energy sources release far fewer greenhouse gases, as well as little or no air pollutants when looked at in their full life cycle. Taking steps to reduce air pollution benefits not only the planet but also human and animal health. Increasing reliance on the Internet of Things (IoT) has helped modernize the energy industry. Sensor attached to generation, transmission, and distribution equipment is used in IoT applications in green energy production. Alternative energy offers several benefits over traditional energy options. As the demand for clean energy grows and environmental prudence becomes the norm, Internet of Things solutions for energy management keep developing. Using the Internet of Things today benefits green energy, enabling companies in this sector to make the most of their data, and improves efficiency and safety.

This chapter intends to explore the necessity of sustainable infrastructure and the key factors affecting sustainability. Sustainable infrastructure constructs the environment to be crucial for society’s survival, health, and comfort. The environment comprises engineering works, buildings, transports, power production facilities, wastewater and water treatment plants, storm water management systems, and even natural systems such as rivers and harbors. The infrastructure systems provide the structures required for the operation of society and facilitate access to goods and services. The demand for infrastructure is rising in tandem with population growth and urbanization structure. The increasing population consumes a large volume of materials and energy to upgrade themselves and their infrastructure. The rising urbanism is adopting the updated information and communication technology (ICT) to transform cities into smart cities. More infrastructure results in some vulnerable effects that create unsustainable burdens. However, more solutions must be implemented at the local level. This study addresses the fundamental science of sustainability, various obstacles inherent in the ICT transformation, recommendations for diplomats, innovators, researchers, scientists, project developers, sponsors, traders, allied stakeholders, and faculties to reach the sustainable infrastructure based on the review of similar works.

Renewable energy (RE) is an essential resource to society for future development globally. An important challenge in the renewable energy systems is weather prediction. Solar and wind are the basic resources of renewable energy, and the generation of power highly depends on weather. Since many efficient techniques are available for weather forecasting, there might be unexpected changes that can influence energy generation. Therefore, advanced technologies must be implemented to avoid these vulnerabilities. Artificial intelligence (AI) techniques help in the analysis, prediction, and control of renewable energy. The recent algorithms of AI support the prediction of energy generation, energy loss, and equipment failures. AI addresses the problems of traditional power generation methods and provides an effective RE system. The integration of Internet of Things (IoT) with AI enables the renewable energy sector to expand to a greater extent. It is not possible to think of modern renewable energy without the contribution of AI and IoT. IoT is equipped with billions of devices that handle the high volumes of data, process a large volume of data, and perform useful functions for people.

In today’s world, we can’t live without energy. It’s essential for the growth and development of the economy. Changes in climate, sustainable growth, health, food security for the world, and environmental protection all require it if we are to make any headway. Governments around the world are looking for innovative ways to generate, control, supply, and save energy because of the rising cost and rising demand for it. Photovoltaic systems, hydropower, wind energy, tidal power, and geothermal energy are examples of traditional renewable energy sources that have advanced significantly in recent years. They, however, are unable to deal with environmental variations. It is critical to developing smart and cost-effective generators in order to meet the advanced world’s energy demands. In this chapter, we introduced the concept of smart energy, smart grid, and smart energy systems in a brief manner. Smart energy portfolio and smart energy management are introduced in the first section. We also discuss how AI and IoT can be used to improve the different energy sources like wind power, solar power, geothermal power, etc.

The massive communication of information over network gadgets associated with the internet trades data starting from one to another with no sort of human cooperation. As innovation is advancing, interconnected organizations give data to each other to impart. The energy utilization is happening at an extremely quick rate, debilitating the assets in delivering it at a similar rate, and the entirety of this requires a transformation to save energy. Information is the focal point of the Internet of Things (IoT), and it has all the information to which there was no entrance before; this information can be utilized in the revolution of the energy management framework. By utilizing advanced IoT innovations, the embracement of renewable can be upgraded significantly further. The reconciliation of IoT in renewable energy is empowering its development by and large.

In the recent digital era, the energy sector in India is truly challenging. But some way or another digital technology has the potential to change the scenario of energy supply in industry. One of the important developments in this decade is the application of Artificial Intelligence (AI). This technology will help us to control smart software and optimize our decision-making and operations. We cannot ignore the need of energy to become sustainable after the introduction of the Internet of Things (IoT). Smart grid technology in IoT is used to detect even minute changes in electricity supply and demand. These two technologies (AI and IOT) jointly provide us a magical tool to improve operational performance in the energy industry. In rural areas, there is a lack of electricity infrastructure supply and demand technologies. A large portion electricity supply is shifting from manufacturing industry to rural areas. They are using grid technology to transform electricity and the load is highly variable. From the demand side, lack of infrastructure and industrial equipment affect consumer devices. An increasing need for electricity in all aspects presents a significant challenge to utilization and cost efficiency. An important issue for the delivery of electricity to rural areas is the infrastructure and administrative policies and regulations. Power plants need to be constructed in rural areas to supply the electricity. This is the modernization of a rural electricity infrastructure. In modernization techniques, smart grid technology can be used to meet low carbon emission and cost-efficiency. It will be interconnected with the traditional grid architecture of electricity energy. Based on recent research, the smart grid should be robust and agile and it might dynamically optimize the grid operations, energy-efficient resources, and so on. Without affecting the nature of village environments, an alternate technology, such as the consumption of solar energy, can also be mutually considered in order to utilize renewable energy. In this chapter we focus on the comparison of traditional and modern technology used for the supply and demand of electricity in rural areas, issues on the implementation of modern technologies, research and development in modernization of electric power systems, and so on.

Technology is evolving at an unbelievable pace, to the extent where many of us can’t keep up effectively. With increasing Artificial Intelligence (AI) complexity, our environment will be transformed in amazing ways over the years and decades that follow. The renewable energy (RE) sector is no different. AI can observe patterns and benefit from large amounts of knowledge. Consequently, AI is able to make improvements to enhance energy production, conversion, and even delivery. These systems allow precise forecasting of, for example, weather and loads, mitigating, among countless other uses, the possibility of electrical surges. AI systems would significantly improve the productivity of renewable systems by automation over the next 10 years. For solar and wind energy, this will become particularly prevalent. Independent power producers would have the latitude required to deliver ever-more sustainable business models and services by integrating increased generation coupled with low-cost savings provided by automation. We are all aware of the requirements of RE, including solar power. However, how can AI help to increase the availability of RE? The demand for global energy is growing day by day, but fossil fuels cannot fulfill our future needs for energy. Because of increased energy consumption, fossil fuel carbon emissions have reached very high levels over time. RE, however, is emerging as a good replacement for fossil fuels. It is safer and also very clean in comparison to traditional sources. The RE industry has made tremendous strides during the preceding decade with developments in technology. AI and machine learning technologies can analyze data to predict the future. So, the use of AI can solve the problems and challenges of RE. In this chapter we discuss RE, its sources and challenges, and how AI can address these challenges.

Geothermal energy is rather one of the oldest forms of renewable sources ever extracted by the human race. Plenty of techniques including dry steam systems, flashing power systems, and binary cycle systems are available to harness geothermal energy to meet the growing demands. Constant upgradation is needed in all these techniques to increase the productivity of the power plants. The advent of the modern computer and the Internet of Things (IoT) created a new era because of the ability of the machine to make smart decisions, and those technologies are effectively used to increase the productivity of geothermal power plants. AI powered IoT facilitates various stages of Geothermal power processing, starting from the identification of GT field for the installation of power plants, optimization of geothermal operations till the dumping of GT waste. Real-time thermodynamic modeling of the energy cycles can also be done using AI, thereby saving manpower, time, and money. Scientific reports show that AI-powered IoT technologies, that includes artificial intelligence (AI) and machine learning (ML), can even be used for GT  fluid forecasting, which is crucial in determining the lifespan of a power plant.

As IoT (Internet of Things) and smart technologies have developed rapidly, many technological advancements have been made possible. The IoT’s main objective is to assist in simplifying processes in a number of different fields, to improve the efficiency of technologies and protocols, and ultimately to improve quality of life. Although IoT technologies can benefit the population in numerous ways, their development must be evaluated from an environmental viewpoint to ensure that global resources are used efficiently and to prevent negative effects. As previously described, considerable research effort is needed to explore the advantages and disadvantages of IoT technologies. Engineering professionals, industrial experts, and academic researchers successfully interacted at the conference. Several key tracks made up the conference, including smart city, energy and environment, e-health, and engineering modeling. Specifically, the editorial covered a number of topics including (i) IoT in sustainable energy and environmental management, (ii) smart cities enabled by IoT, (iii) ambient assisted living, and (iv) IoT technologies for transportation and low-carbon products. An important outcome of our introductory analysis has been a greater understanding of both the scientific developments in IoT applications and the potential ecological consequences associated with increasing IoT applications.