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Office buildings built before the entry into force of the first thermal regulation in 1991 constitute a relevant group for analysing the energy performance of the Portuguese building sector. A dynamic energy simulation was used to assess the energy performance of an existing office building located in the town of Bragança, Portugal. Four energy efficiency measures were selected and a financial evaluation through the internal rate of return (IRR) method was undertaken to choose the best retrofit option for improving the building’s energy performance. An investment package consisting of the roof insulation and a new equipment for the domestic hot water system presented an IRR higher than the discount rate used in the analysis, and, thus, a positive financial return. The results of the study also suggest that the EU’s comparative methodology framework is not particularly suitable for assessing building retrofit investment at the private investor’s perspective and further refinement in the cost-effective approach to renovations is needed to help stimulate building’s energy renovation market. Suggestions for further studies conducted for office buildings in the different climate zones in Portugal are also proposed.

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With the increasing size of wind power generation it is required to perform power system stability analysis that uses dynamic wind generator models.In this paper are presented all the wind power system components, including the turbine, the generator, the power electronic converter and controllers. The aim is to study the Doubly Fed Induction Generator (DFIG) operation and its connection to the power system, either during normal operation or during transient grid fault events. Two different control system design technologies are present, the first is performed by standard PI controllers and the second is based on artificial neural networks.

The Maximum Power Point Tracking (MPPT) is an important factor to increase the efficiency of the solar photovoltaic (PV) system. This paper presents a solar PV system containing a solar PV array, a DC/DC boost converter and a load. Different MPPT algorithms have been established with their features. The conventional algorithms (Perturb and Observe, Incremental Conductance and Open Circuit Voltage) show a lot of drawbacks. The major issue is the tracking of the Maximum Power Point (MPP) when environmental conditions change faster. So, a MPPT technique based on Neural Network (NN) was developed and which can enhance the efficiency and gathers the advantages of a lot of techniques. A multi layer neural network with back-propagation algorithm is used in order to have a small Mean Squared Error (MSE). The inputs of NN are irradiance, temperature and the output is the duty cycle that controls the boost converter. Finally, it is discussed the results and made comparison in terms of performance of the different algorithms, covering the overshoot, time response, oscillation and stability.