One of the main challenges of today’s society is the need to fulfill at the same time the two sides of the dichotomy between the growing energy demand and the need to look after the environment. Smart Grids are one of the answers: intelligent energy grids which retrieve data about the environment through extensive sensor networks and react accordingly to optimize resource consumption. In order to do this, the Smart Grids need to understand the existing relationship between energy demand and a set of relevant climatic variables. All smart “systems” (buildings, cities, homes, consumers, etc.) have the potential to employ their intelligence for self-adaptation to climate conditions. After introducing the Smart World, a global framework for the collaboration of these smart systems, this paper presents the relationship found at experimental level between a range of relevant weather variables and electric power demand patterns, presenting a case study using an agent-based system, and emphasizing the need to consider this relationship in certain Smart World (and specifically Smart Grid and microgrid) applications.

The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG) is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF) in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.

This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.

Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.

This paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coverage.

Context-aware applications aim at providing personalized services to end users. Sensors and context sources are able to provide enormous amounts of valuable information about individuals that can be used to drive the behavior of services and applications, and adapt them to the specific conditions and preferences of each user. Thanks to advances in mobility, convergence and integration, increasingly larger amounts of these data are available in the Internet. However, this context information is usually fragmented, and traditionally applications have had to take care of context management themselves. This work presents a solution for a converged context management framework and how it can be employed in a future Internet to integrate data from all context sources and serve it to client applications in a seamless and transparent manner. This framework takes advantage of the intelligent and convergent features of next-generation networks, allowing seamless integration, monitoring, and control of heterogeneous sensors and devices under a single context-aware service layer. This layer is centered on a context intelligence module, capable of combining clustering algorithms and semantics to learn from user usage history and take advantage of that information to infer missing or high-level context data.