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This paper aims to contribute to the urgent reflection as a society about environmental protection, in the ultimate challenge that is the sustainable use of energy resources. Since Portugal is at an early stage of market development internally, governmental and local stimulation policies play a central role and are a key element in the successful diffusion of Electric Mobility. The study will focus on the transition of a company car fleet, which currently consists of combustion vehicles, to electric vehicles. With this change it becomes necessary to understand how the electrical installation will be affected due to the installation of charging stations, allowing the company to have some autonomy from the public grid. The various changes resulting from the installation consumption profile will be studied and compared. The state of the art, the level of maturity and where the development of electric mobility in Portugal is heading will also be appreciated.

Energy efficiency in buildings are an important domain regarding its impact in the global energy consumption. Several efficiency measures are currently available, using different methodologies. In this paper an approach using intelligent systems is implemented through a smart controller able to automatically and efficiently manage building's energy. This smart controller uses Fuzzy Logic Control (FLC) as a decision maker and is supported by Artificial Neural Networks (ANN) used to forecast the building outside temperature. In addition, and in order to increase overall energy efficiency, renewable energies associated with a storage system are considered. For the designed smart controller implementation, a thermal house model is simulated using a MATLAB toolbox. In this paper emphasis is given to the building´s Heating Ventilation and Air Conditioning (HVAC) control and a rough economic analysis is drawn, allowing to infer about the adopted efficiency measures impact.

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited energy resource using a demand-side management approach. The building divisions are modeled using an electro-thermal modular scheme. For control purposes, this modular scheme allows an easy modeling of buildings with different plans where adjacent areas can thermally interact. The control objective of each subsystem is to minimize the energy cost while maintaining the indoor temperature in the selected comfort bounds. In a distributed coordinated environment, the control uses multiple dynamically coupled agents (one for each subsystem/zone) aiming to achieve satisfaction of available energy coupling constraints. The system is simulated with two zones in a distributed environment.

The aim of the paper is to present a demand-side management approach that uses a distributed model-based predictive controller to provide indoor thermal comfort in buildings with a limited green energy resource. The overall system predicts the indoor environmental conditions for buildings with different plans that are modeled using an electro-thermal modular scheme. For control purposes, this modular scheme allows an easy modeling of buildings with different plans where adjacent areas can thermally interact. The control system selects the most appropriate actions to satisfy the comfort and power constraints. In a distributed coordinated environment, the control uses multiple dynamically coupled agents (one for each subsystem/zone/house) aiming to achieve satisfaction of available energy coupling constraints. The distributed environment is simulated with two houses with different plans, one house with two divisions and the other with one. The results validate the proposed methodology in terms of both thermal comfort and energy savings.