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The COVID-19 pandemic has caused problems all around the world. To control the spread of the virus, some governments have imposed restrictions on the mobility of their citizens. Specifically, in Spain, from the months of March to May, lockdown was imposed. The effect of these actions is reflected not only on epidemiological data, but also on the behavior of the population, and therefore, some of their sectors. One of these sectors is the energy consumption. In this sense, this paper studies the impact that the COVID-19 pandemic has had on energy consumption using the data from the Smart Meters of the secondary distribution network at low-voltage. This approach has de advantage that is not only able to obtain the impact at aggregated level but is also able to obtain the impact on smaller groups at customer level. The results show an increase in energy consumption on residential customers in contrast with a remarkable reduction on non-residential customers. In the case of non-residential customers, different consumption patterns have been found during the pandemic. These consumption patterns are highly correlated with the restrictions imposed to control de spread of the pandemic. Ministerio de Ciencia, Innovación y Universidades, Government of Spain project “Bigdata Analitycs e Instrumentación Cyberfísica para Soporte de Operaciones de Distribución en la Smart Grid” RTI2018-094917-B-I00 Ministerio de Educación y Formación Profesional , Government of Spain “Formación de Profesorado Universitario (FPU)”, Grant Number FPU16/03522

Electric power system is one of the most critical a nd strategic infrastructures of industrial societies. Nowadays, it is necessary the modernization and automation of the electric power grid to increase energy efficiency, reduce emissions, and transit to renewable energy. Power utilities face the challenge of using information and communication networks more effectively to manage the demand, generation, transmission, and distribution of their commodity services. Communication network constitutes the core of the electric system automation applications, the design of a cost-effective, and reliable network architecture is crucial. To resolve this difficulty in this work we study the integration of advanced artificial intelligence technology into existing network management system. This work focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We have normalized the knowledge management base necessary to manage the current resources in the telecommunication networks. Intelligent agents learn the normal behaviour of each measurement variable and combine the intelligent knowledge for the management of the network resources. We present an analysis of corporate network management requirements and technologies, together with our implementation experience with the development of an integrated management system for a company network.

The increasing of the storage system capacity and the reduction of the access time have allowed the development of new technologies which have afforded solutions for the automatic treatment of great databases. In this chapter a methodology to create Enterprise Information Systems which are capable of using all information available about customers is proposed. As example of utilization of this methodology, an Enterprise Information System for classification of customer problems is proposed. This EIS implements several technologies. Data Warehousing and Data Mining are two technologies which can analyze automatically corporative databases. Integration of these two technologies is proposed by the present work together with a rule based expert system to classify the utility consumption through the information stored in corporative databases.