Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

In Italy, the calculation of the energy needs of buildings has been mainly based on quasi-steady state calculation procedures. Nowadays, the increasing interest in more detailed energy analysis for high-efficiency buildings requires more accurate calculation methods. In this work, starting from the hourly data of UNI 10349, the downscaling of a typical meteorological year was carried out by applying different mathematical and physical models for the main climate variables considered in the energy balance of a building to be used in dynamic simulation tools. All results were validated with one-minute measurements observed at the ENEA Research Centre in Rome, Italy. The results showed an MBE% of 0.008% and RMSE% of 0.114% using the interpolation spline method for the temperature, while, for the global horizontal irradiance, applying the novel sinusoidal–physical interpolation method showed an MBE% of −0.4% and an RMSE% of 31.8% for the 1 min observation data. In this paper, an easily implemented novel model for downscaling solar irradiance for all sky conditions that takes into account the physical aspects of atmospheric phenomena is presented.

This paper presents the bi-dimensional modelling of a thermal energy storage (TES) facility, ideated, realized and tested by ENEA (Italy). This prototype enabled the thermocline storage with molten salts in an innovative geometry including two vertical channels to force the circulation through two spiral heat exchangers, realized respectively for charging and discharging phases. A thermo-physical model was built and tested for this particular geometry in order to analyse the temperature distribution along the radius. The numerical results well reproduced the experimental values, particularly if the start-up period is excluded from the simulation. An analytical solution was also produced to provide a short-cut methodology able to evaluate the thermocline (along the vertical axis) depending on both the time and the radius values (2D variation). Hence, the thermocline degradation was studied accurately by taking the radius value as sensitivity parameter. It was found that at the edges (r→1) the thermocline does not “degrade” and does not vary for times 10 times greater with respect to the centre of the tank (r→0). The numerical modelling can be useful for the process analysis, for scaling-up the thermal storage system and for evaluating the system reliability for industrial plants (Solar-to-Electricity) from a technical-economical point of view.