2. Deep Technologies Using Big Data in: Energy and Waste Management

The potential of big data is growing exponentially, and over the next decade, it will almost change every business and industry. The diverse applications of big data have opened new dimensions for industrialization and urbanization. This leads to a huge energy crisis and environmental wastes. The conventional methods are incapable of handling energy crises and wastes. Moreover, the methods require a lot of manpower and resources which make them more costly. Thus, the ultimate solution of handling this computationally expensive and complex issue is by analyzing the demand of energy, classification, and identification of the source of wastes at every step of technological advances. Big data analytics is capable of analyzing these large datasets, still faces major setbacks because of high-dimensional, imbalance, and dynamic datasets. These difficulties lead to various other problems, such as search-based data analytics problems, multi-objective optimization problems, uncertain data problems, and classification and clustering problems. To address these issues, various data analytics tools and statistical techniques were designed by the researchers; but, the literature shows strong evidence that deep learning techniques are efficient in solving these complex and computationally expensive problems very well. This chapter attempts to exploit deep learning techniques to solve energy and waste management issues through big data analytics. Finally, the chapter concludes with the discussion and prospects of deep learning in big data analytics for energy and waste management.