Special Issue on Applications of Artificial Neural Networks for Energy Systems

Giuseppe Marco Tina

doi:10.3390/app9183734

Abstract

In the 21st century, newer and cleaner technologies like renewable energy technologies, fuel cells, and smart grids are promising to transform the way we generate and distribute electric energy [...]

Highlights

In the 21st century, newer and cleaner technologies like renewable energy technologies, fuel cells, and smart grids are promising to transform the way we generate and distribute electric energy.Long-term and ambitious commitments and stable regulatory policies have established renewable energy as a major option in the energy sector in almost all parts of the world.For example, in 2018, within a climate and energy framework that applies until 2030, the EU decided to develop a clean, cheap, and reliable energy system
Greenhouse gas emissions must decrease by at least 40% compared to 1990 levels, and renewable energy sources like wind and solar must provide 32% of our energy
The global goal for renewable energy translates into a 57% share of renewable energy in the European energy sector by 2030, which implies that wind and solar will reshape the European energy system

Summary

Introduction

In the 21st century, newer and cleaner technologies like renewable energy technologies, fuel cells, and smart grids are promising to transform the way we generate and distribute electric energy. In 2018, within a climate and energy framework that applies until 2030, the EU decided to develop a clean, cheap, and reliable energy system To this end, greenhouse gas emissions must decrease by at least 40% compared to 1990 levels, and renewable energy sources like wind and solar must provide 32% of our energy. The global goal for renewable energy translates into a 57% share of renewable energy in the European energy sector by 2030, which implies that wind and solar will reshape the European energy system Such a target for greenhouse gases will impact drastically the presence of thermoelectric power plants, as coal demand will be cut by two thirds, and oil and gas demand will be cut by a quarter. Artificial neural-networks have useful specific features, such as the ability to learn the key information patterns within a multi-dimensional information domain, being fault-tolerant, robust, and possessing noise-immunity [1]

Artificial Neural Networks for Energy Systems

Future Energy System and Artificial Intelligence

Findings

Methods