Short-term prediction of PV output based on weather classification and SSA-ELM

Abstract

In this paper, according to the power output characteristics of distributed photovoltaic users, the SSA-ELM (Sparrow Search Algorithm - Extreme Learning Machine) model based on weather type division is proposed for photovoltaic power day ahead prediction. Because the solar panel power generation sequence of photovoltaic users contains high frequency fluctuations, in this paper we use the power sequence convergence effect to make cluster prediction on all photovoltaic panels to reduce the randomness of distributed photovoltaic. The prediction accuracy is further improved by dividing weather types. The historical data of distributed PV users in a region of Gansu province is used for modeling verification, and the results show that the prediction error of the proposed method is lower. In bad weather, the root mean square error is at least 0.02 less than the comparison model, and the average annual accuracy rate is 93.2%, which proves the applicability of the proposed method in different output types.

Weather Classification for PV System and its Validation in Real-world Conditions

“…The existing body of literature on weather classification for PV systems presents a diverse range of methodologies and findings [1][2][3][4]. Early research primarily focused on the impact of basic weather conditions like solar irradiance and temperature on the efficiency of PV system.…”

Impact of Electric Vehicles in Mitigating Damages of Typhoon in urban power systems

“…The existing body of literature on weather classification for PV systems presents a diverse range of methodologies and findings [1][2][3][4]. Early research primarily focused on the impact of basic weather conditions like solar irradiance and temperature on the efficiency of PV system.…”