Abstract
Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

Abstract
This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.

Abstract

Abstract
This paper presents a comprehensive sensitivity analysis to identify the uncertain parameters which significantly influence the decision-making process in distributed generation (DG) investments and quantify their degree of influence. To perform the analysis, a DG investment planning model is formulated as a novel multistage and multiscenario optimization problem. Moreover, to ensure tractability and make use of exact solution methods, the entire problem is kept as a mixed-integer linear programming optimization. A real-world distribution network system is used to carry out the analysis. The results of the analysis generally show that uncertainty as well as operational variability of the considered parameters have meaningful impacts on investment decisions of DG. The degree of influence varies from one parameter to another. But, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. Hence, the analysismade in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

Abstract
Higher education institutions are at this stage, on the one hand, faced with challenges never seen before and, on the other hand, their action is moving very rapidly into digital learning spaces. These challenges are increasingly complex because of the global competition for resources, students and teachers. In addition, the amount of data produced inside and outside higher education institutions has grown exponentially, so more and more institutions are exploring the potential of Big Data to meet these challenges. In this context, higher education institutions and key stakeholders (students, teachers, and governance) can derive multiple benefits from learning analytics using different data analysis strategies to produce summative, real-time and predictive information and recommendations. However, it may be questioned whether institutions, academic administrative staff as well as including those with responsibility for governance, are prepared for learning analytics? As a response to the question raised in this paper is presented an extension of a disruptive conceptual approach to higher education, using information gathered by IoT and based on Big Data & Cloud Computing and Learning Analytics analysis tools, with the main focus on the stakeholder governance.

Abstract
The integration of social media in education has been raising new challenges for teachers, students and organizations, in both traditional and technology-mediated learnings settings. Formal higher education contexts are still mostly anchored and locked up in institutional LMS, despite the innumerous educational digressions that educators have been conducting throughout social media networks. One of the biggest challenges in contemporary educational needs consists on managing the integration, validation and reporting on educational processes, goals and student performance, when they are widely spread in several formal and informal contexts. In this chapter a system for the integration of LMS and social media is presented, as well as evidence on its practical usage. A set of social network analytics are also brought forward as features that are currently being added to the referred system.