Applied artificial intelligence

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management.

The fear that jobs will be destroyed by automation in the form artificial intelligence or robots is widespread. It is difficult to estimate how serious the effect of automation will be employment. Different studies and surveys reveal various estimations of how several jobs will be lost by automation in the form artificial intelligence, while economists and politicians say that artificial intelligence creates as many employment opportunities as it destroys; therefore, equilibrium is maintained in employment in the long-run. Ever since the time we had Industrial Revolution, people have always had worries about their jobs being taken away by machines. Every new technology has always brought fears about employment, starting from the weaving looms that were the first to be automated in the 1820s to the start of internet two decades ago [1]. Most people agree that there is a great threat posed to employment today by technological advances such as artificial intelligence. Beings that are artificially intelligent like robots are a unique order of machine. They do make things in an easier way and more a wrench in the very life wheel. Several studies on the impact of artificial intelligence paint a future picture that is very depressing. For example, one paper by researchers from The Oxford University foresees that 47% of jobs in the world are at high risk of artificial intelligence over the next 20 years.All kind of employment could fall by the wayside; this includes jobs in logistics and transport, mining, construction, police force, and food preparation. The study also found that even the roles that people think of as " high value " such as lawyers and doctors will also be undermined by artificial intelligence. The question of what people will do after losing their jobs to artificial intelligence rises. Perhaps, if people will be hardworking and inventive, they will get other activities that are productive. Perhaps people will perform fewer duties and get less pay [2].