Filippo Spertino

Compressive Spatio-Temporal Forecasting of Meteorological Quantities and Photovoltaic Power

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of stations. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.

Operational characteristics of a 27-MW wind farm from experimental data

The diffusion of wind systems is emerging in several countries, with solutions of different type and size. The analysis of experimental results gathered on-site is essential to understand the characteristics of the wind systems and of their interconnection to the grid. The basic aspects of the experimental analyses concern availability of the wind resource, efficiency of the plants, and power quality of the grid connected wind system. This paper illustrates and discusses the results obtained from monitoring a 27-MW wind farm with SCADA systems and network analysers. A dedicated procedure for efficiency evaluation of the wind turbines is presented, whose results underline evident mismatches between the actual performance and the data provided by the manufacturer.

Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of stations. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.