The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw data into knowledge represented by the DIKW hierarchy and the NIST Big Data interoperability model. Finally, a binding element between the two views is represented by human expertise and skills to obtain a deeper understanding of the results and transform knowledge into wisdom. Our new view faces the challenges arriving in energy markets by adding a binding element that gives support for optimal and efficient decision-making. To show how our framework works, we developed a case study. The case implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP). The MAPE for some of the REP’s markets was less than $5\%$. In addition, the case shows the effect of the binding element as it raises new development alternatives and becomes a feedback mechanism for more assertive decision making.

In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of information and the generation of valuable knowledge from raw data that can support data-driven decisions for operating on the grid. This paper presents the results of the big data implementation and data analytics techniques in a case study with smart metering data from the city of London. Implemented big data and data analytic techniques to show how to understand user consumption patterns on a broader horizon, the relationships with seasonal variables identify behaviors related to specific events and atypical consumptions. This knowledge helps support decision making about improving demand response programs and, in general, the planning and operation of the Smart Grid.

This paper presents the implementation of a controller based on Adaptive Task Allocation Algorithm (ATAA) for a traffic network with signalized intersections, where it is required to decrease waiting times of vehicles at every intersection. The main contributions of this work are the implementation of ATAA controller under a Model Predictive Control (MPC) scheme and the use of an estimator which does not require an explicit model of the system to predict its future behavior. Two case studies are presented and implementation results are compared for scenarios using both ATAA with open loop controller and the MPC proposed. For both case studies improvements up to 31.3% were obtained for vehicles' mean delay time with the MPC approach in contrast to a network with no traffic light control.

This article shows the results of an applied investigation aimed to establish a general design methodologyfor the modernization of automated systems oriented to modernization of physical and technological infrastructureof the “Servicio Nacional de Aprendizaje” (SENA) in Colombia, with systems, equipment and applicationsoriented to sustainability, efficiency and energy savings. The objective of this work is to "Propose amethodology for the conceptual design of automated building systems". The methodology was applied to thedesign of the automated system of the Technological Services Laboratory (LST) of the Center for Electricityand Industrial Automation (CEAI)- SENA and its implementation constitutes a reference model for the otherSENA training centers to apply in order to improve the conditions of safety, comfort and energy managementin their facilities.