Interval forecasting strategy of photovoltaic generation considering multi-factor self-fluctuation

Abstract

Nowadays, interval forecasting is crucial for the power dispatching department because it can provide reliable boundaries of photovoltaic (PV) generation. However, few countermeasures are proposed to avoid the multi-factor self-fluctuation involved in the forecasting. To enhance the credibility of PV interval forecasting, this paper mainly conducts the following research around two factors “temperature” and “solar irradiance”: 1. Considering the periodicity of temperature data, an interval forecasting strategy based on auto-regressive integrated moving average model (ARIMA) and Bootstrap algorithms is designed to obtain its fluctuation range. 2. For solar irradiance data, considering it has temporal and non-temporal features, an interval forecasting strategy based on interval optimization and multi-extreme learning machine (ELM) is designed to obtain its fluctuation range; 3. Based on the obtained fluctuation boundaries of temperature and solar irradiance, the ELM is combined with the empirical mode decomposition (EMD) to complete error fitting and apply it to complete the interval forecasting of PV generation. Related case studies are presented in this paper. Finally, on the basis of ensuring the interval coverage requirement, we find the proposed method improves the forecast performance by 7.5 % compared to the existing methods at least, which will provide more reliable data for decision-making and management.