1. Introduction

Recently, many national and international communities and authorities developed energy efficiency strategies and programs in order to reduce energy poverty. In Ref. [1], European Economic and Social Committee (EESC) stated that more than 50 million Europeans are affected by energy poverty in 2009. EESC also recommends to establish a European poverty observatory that will bring together all stakeholders to take correct measures to reduce the gaps between different countries and regions in terms of energy poverty and propose a set of statistics indicators to monitor the evolution of energy efficiency. EESC draw attention that energy prices are constantly increasing with more than 10% annually, while most of the Europeans spending an increasing share of their income on energy.

© 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

In 2012, European Commission adopted Energy Efficiency Directive that proposed measures to increase with 20% energy efficiency target by 2020 [2]. On November 30, 2016, the Commission updated the Directive, by targeting 30% energy efficiency for 2030. The proposed measures for energy are oriented toward increasing consumers' awareness regarding their consumption management through electronic bills and information and communications technology (ICT) solutions, encourage them to become prosumers by investing in their own generation sources such as photovoltaic (PV) panels, wind turbines and storage devices.

The main objective of the chapter is to present an implementation of artificial neural networks (ANNs) for the electricity consumption management based on smart metering (SM) data. This objective will be reached by following topics:


The ANN performance will be compared with stochastic methods (classification, ARMA and ARIMA models) and the best solution is adopted for ICT prototype.
