Prediction Intervals for Day-Ahead Photovoltaic Power Forecasts with Non-Parametric and Parametric Distributions

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%

Regional Forecasts of Photovoltaic Power Generation According to Different Data Availability Scenarios: A Study of 4 Methods

The development of methods to forecast PV power generation regionally is of utmost importance to support the spread of such power systems in current power grids. The objective of this study is to propose and to evaluate methods to forecast regional PV power one-day ahead of time and to compare their performances. Four forecast methods were regarded of which 2 are new ones proposed in this study. Together they characterize a set of forecast methods that can be applied in different scenarios regarding availability of data and infrastructure to make the forecasts. The forecast methods were based on the use of support vector regression and weather prediction data. Evaluations were done for 1 year of hourly forecasts using data of 273 PV systems installed in 2 adjacent regions in Japan, Kanto and Chubu. The results show the importance of selecting the proper forecast method regarding the region characteristics. For Chubu, the region with a variety of weather conditions, the forecast methods based on single systems’ forecasts and the one based on stratified sampling provided the best results. In this case the best annual normalized RMSE and MAE were 0.25 kWh/kWhavg and 0.15 kWh/kWhavg. For Kanto, with homogeneous weather conditions, the 4 methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWhavg for the normalized RMSE and 0.202 kWh/kWhavg for the normalized MAE

Day-Ahead Scheduling for Supply-Demand-Storage Balancing –Model Predictive Generation with Interval Prediction of Photovoltaics

International audienc

Power system operation with battery charge/discharge scheduling based on interval analysis

The use of photovoltaic (PV) generation forecasts in economic load dispatching control, which includes the unit commitment of conventional power plants, is essential to ensure the economic performance and the reliability of power systems. In the previous study, we developed a day-ahead charge and discharge scheduling method of battery energy storage systems based on interval analysis using prediction intervals of a PV generation forecast; this interval forecast considers forecast errors and gives not only the forecasted output but also the possible range of the actual output with a certain confidence. In this study, we evaluate the proposed scheduling method by numerical simulations in terms of the power system supply and demand operation