There are several ways to obtain forecasts of photovoltaic, PV, power depending on the required accuracy, forecast horizon, climate and other conditions. In this study, we evaluate 3 strategies to obtain one-day-ahead regional forecasts of PV power generation. The strategies were based on the use of weather forecast, past PV power generation and support vector regression. The strategies characterize different scenarios regarding what data are available to make the forecasts, and how these data are used. The first strategy, Strategy 1, is based on a scenario where local PV power generation data can be obtained. Strategy 2 is based on a scenario where regional PV power generation data are available, and all related weather forecast data are used. Strategy 3 is derived from the second one, but it used a principal component analysis to select only weather forecast data that is relevant to the forecasts. To evaluate the strategies data of 6 PV systems, 149 kWh of installed capacity, in different locations in Hokkaido, Japan, were used to make 1 year of forecasts. The annual results show a maximum variation of 9.3% of the forecast error among the strategies. Strategy 3 was the best, yielding a RMSE of 10.24 kWh, 2.7% lower than the one achieved with Strategy 1. On the other hand, Strategy 2 was the one with the worst annual performance with a RMSE of 11.16 kWh. Looking at the results in a monthly fashion the same trend occurs with Strategy 3 being the one with the lowest error in 9 of the 12 months analyzed. The results show that the use of principal component analysis can yield meaningful improvement of the regional forecast error. Moreover, the results give a preliminary assessment of the level of accuracy that can be achieved not only with this technique, but also with feasible strategies to forecast regional PV power.
Da Silva Fonseca, J. G., Oozeki, T., Ohtake, H., Shimose, K. I., Takashima, T., & Ogimoto, K. (2014). Forecasting regional photovoltaic power generation - A comparison of strategies to obtain one-day-ahead data. In Energy Procedia (Vol. 57, pp. 1337–1345). Elsevier Ltd. https://doi.org/10.1016/j.egypro.2014.10.124