An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Abstract

Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.Index Terms-Aerosol index (AI), artificial neural network (ANN) method, back propagation (BP) network, maximum absolute prediction error, photovoltaic (PV) power forecasting.

A review of forecasting algorithms and energy management strategies for microgrids

“…Mellit, Massi Pavan, and Lughi (2014) focused on solving the short-term solar power forecasting problem in a large-scale PV plant located in Southern Italy, where three distinct ANN models were developed for three weather types (sunny, partly cloudy and overcast). In reference (Liu, Fang, Zhang, & Yang, 2015), an improved solar power forecasting model was developed to enhance forecasting accuracy under the extremely weather condition, where the aerosol index was regarded as a key component to indicate solar radiation attenuation. The estimated results demonstrated its superiority over the conventional model with the consideration of temperature, humidity and wind speed data for the forecasting results.…”

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