Geospatial is going to be the absolute heart of making sense of trillions of bits of data that are going to be surveyed by big machines. The buzz word of the last 4-5 years has been artificial intelligence (AI) and is influencing every marketplace including GIS, healthcare, pollution, and the list is truly endless. It is the world of collaborative and multidisciplinary research where technology is applied in almost every domain and has proved extremely useful to end-users. The diversity of themes identified in this chapter can be grouped into the categories of renewable energy mapping: spatiotemporal analysis, and data mining. This chapter gives a comprehensive account of transformation from the classical ML clustering techniques with the potential of quantum clustering (QC) which can be applied or mapped to renewable energy solutions driven by use of GIS, or narrating the importance of GIS and quantum. This chapter highlights the relationship between GeoAI, cybersecurity, and quantum computing in the world of spatial data.
Top1. Introduction
History tells us that the value shift is triggered by a new perspective on the way of life. AI, the Internet of Things (IoT) and big data are changing economies, industries, societies and our lifestyles. The revolution is manifold and by far the most advanced with breakthroughs in sectors such as 3D printing, robotics, energy, blockchain, autonomous vehicles and more. Automation drastically upgrades the efficiency of organization and businesses empower people all over the world.
AI will become an absolute enabler in the utility of the future. The state-of-art technology is used to get insight from the volume of data to harvest from energy system. AI tools have the ability to identify patterns and trends within energy sets of data. From that energy data we can start making analytics and predictions. Generated insights and predictions can help to enact traders’ decision support systems. The end-to-end story of an AI solutions, it could fit anywhere in the processes and operational chain of the energy provider companies. AI solutions helps in such a way that managing assets, trading, forecasts of the needs for the future. The good quality data i.e. weather data, geographical assets data, historical demand data, prices data, and supply data for the AI analytics to pickup patterns. The terminology ML further uses for decision making and optimization of energy system operations. The paper addresses uses of various ML techniques used to solve issues related to integration and generation of renewable energy (Adeniyi, 2016).
Expectations from AI:
Geospatial data and technologies aspire to be an integral part of the disruptive journey. Imagine a world where smart, connected and effective monitoring will make breathing easier for us and our surroundings more liveable. A map is a way of organizing all the information related to everybody’s place. There is an entire information ecosystem that we have access to like never before. Anyone can broadcast their location and that is revolutionary. Geospatial technology approach can helpful to near-term opportunities to assessed regional and national energy targets for renewables. The design distribution and control of the energy system is highly complex and to address generation and storage of energy. The geospatial technology and AI combined model gives variety of options to improve smart distribution of energy surveillance the energy demand [49]. The tones of high resolution input data from the turbines via satellite to cloud can be stored every day. To handle such type of data, scientist move from classical techniques to quantum technology (Zhou,2021).
This chapter addresses how the renewable energy mapped with GeoAI and quantum technology. Section 2 covers basics of ML and approach of clustering with the help of classical clustering algorithm. And also discusses about strength of AI and ML for geospatial and renewable energy industry. Section 3 includes the concept of quantum computer, comparison between classical and quantum computer, and the glimpse of QML area for better understanding of quantum technology. Lastly the conclusion and discussion section 4 discuss about the use of and future scope of quantum technology in renewable energy sectors.