Glazing surfaces strongly affect the building energy balance considering heat losses, solar gains and daylighting. Appropriate operation of the screens is required to control the transmitted solar radiation, preventing internal overheating while assuring visual comfort. Consequently, in the building design phase, solar control systems have become crucial devices to achieve high energy standards. An operation based on well-defined control strategies can help to reduce cooling energy consumption and ensure appropriate levels of natural lighting. The present study aims at investigating the effect of smart screening strategies on the energy consumption of a test building designed in the Mediterranean climate. With the aim of automatically setting the inclination of venetian blind slats, the necessary equations are analytically found out and applied. Equations obtained are based on the position of the sun with respect to the wall orientation. In the case of a cloudy day or an unlit surface, empirical laws are determined to optimize the shielding. These are extrapolated through energy simulations conducted with the EnergyPlus software. Finally, using the same software, the actual benefits obtained by the method used are assessed, in terms of energy and CO₂ emissions saved in a test environment.

The day-ahead photovoltaic electricity forecast is increasingly necessary for grid operators and for energy communities. In the present work, the hourly PV production is estimated using two models based on feedforward neural networks (FFNNs). Most existing models use solar radiation as an input. Instead, the models proposed here use numerical weather prediction (NWP) data: ambient temperature, relative humidity, and wind speed, which are easily accessible to anyone. The first proposed model uses multiple inputs, while the second one uses only the necessary information. A sensitivity analysis allows for the identification of the variables that are most influential on the estimation accuracy. This study concludes that the hourly temperature trend is the most important variable for prediction. The models’ accuracy was tested using experimental and NWP data, with the second model having almost the same accuracy as the first despite using fewer input data. The results obtained using experimental data as inputs show a coefficient of determination (R²) of 0.95 for the hourly PV energy produced. The RMSE is about 6.4% of the panel peak power. When NWP data are used as inputs, R² is 0.879 and the RMSE is 10.5%. These models can have a significant impact by enabling individual energy communities to make their forecasts, resulting in energy savings and increased self-consumed energy.