Innovative Advances in Intelligent Energy Systems

Aims & Scope

Innovative advances in machine intelligence (Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Big Data, IoT, etc.) in energy are improving the way the world creates, sells, and consumes energy at a time when the environmental impacts of the global power grid are under constant scrutiny. Industries are using these intelligent algorithms to take deep dives into data that can drive better decision making, cost advantages, and predictions that can stave off energy disasters and expensive downtime. On the whole, the global energy sector produces an incredulous amount of data. It is impossible for humans to manually extract and analyze enough of these data stores to arrive at meaningful conclusions and predictions. Artificial intelligence, in particular machine learning, is taking on a vital role in making sense of massive amounts of energy-related data.

Global electricity demand is set to triple by 2050. Machine intelligence approaches have the potential to reduce the wastage of energy, increase energy efficiency, lower energy costs, and facilitate and accelerate the use of clean renewable energy sources in power grids worldwide. They can also improve the planning, operation, and control of power systems. If we are to reach key milestones set by the main net zero scenarios, intelligent energy systems are crucial. Research and development in this area is rapidly growing and is only expecting to continue as consumer demand rises.

The Innovative Advances in Intelligent Energy Systems series will provide a framework for the fusion of machine intelligence approaches with the areas of energy demand, consumption forecasts, transparent big data-driven pricing strategy, Energy disaggregation, Intelligent reporting, optimizing energy consumption, and analysing renewable sources. It will offer the latest technological advances, insights and analyses from across the globe, supporting the development of next generation energy systems.

The proposed book series will cover topics within the following main areas:

1. Artificial Intelligence enabled grid management

2. Smart Grid with Intelligent Storage

3. Machine intelligence for forecasting, prediction and maintenance

4. Anticipating energy demand

5. Machine intelligence for energy planning, demand and control

6. Integrating IoT devices and deep learning for renewable energy in big data system

7. AI-Driven IoT for wind and solar energy

8. Full autonomy of energy systems

Audience

Primary market/audience: Researchers and engineers working in the areas of power systems, intelligent energy systems and renewable energy.

Secondary market/audience: Graduates studying energy, sustainability, energy engineering and technology etc.

Publish a book with Elsevier

• Information for Authors

• Propose a book

• Contact an Acquisitions Editor

Series Volumes

• Computer Vision and Machine Intelligence for Renewable Energy Systems

• Green Machine Learning and Big Data for Smart Grids