The fIrst oil crisis of 1973-74 and the questions it raised in the economic and social fIelds drew attention to energy issues. Industrial societies, accustomed for two decades or more to energy sufficiently easy to produce and cheap to consume that it was thought to be inexhaustible, began to question their energy future. The studies undertaken at that time, and since, on a national, regional, or world level were over-optimistic. The problem seemed simple enough to solve. On the one hand, a certain number of resources: coal, the abundance of which was discovered, or rather rediscovered oil, source of all the problems ... In fact, the problems seemed to come, if not from oil itself (an easy explanation), then from those who produced it without really owning it, and from those who owned it without really control ling it natural gas, second only to oil and less compromised uranium, all of whose promises had not been kept, but whose resources were not in question solar energy, multiform and really inexhaustible thermonuclear fusion, and geothermal energy, etc. On the other hand, energy consumption, though excessive perhaps, was symbolic of progress, development, and increased well being. The originality of the energy policies set up since 1974 lies in the fact they no longer aimed to produce (or import) more, but to consume less. They sought, and still seek, what might be emphatically called the control of energy consump tion, or rather the control of energy demand.

As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms. Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.

Explores trends and projections in energy supply and demand using real-life case studies and modeling techniques.

Energy for Sustainable Development: Demand, Supply, Conversion and Management presents a comprehensive look at recent developments and provides guidance on energy demand, supply, analysis and forecasting of modern energy technologies for sustainable energy conversion. The book analyzes energy management techniques and the economic and environmental impact of energy usage and storage. Including modern theories and the latest technologies used in the conversion of energy for traditional fossil fuels and renewable energy sources, this book provides a valuable reference on recent innovations. Researchers, engineers and policymakers will find this book to be a comprehensive guide on modern theories and technologies for sustainable development.