Abstract
Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation.
Highlights
Energy, especially electrical energy, plays a key role in a country’s development; it improves the living standard of people
The datasets for training and testing were normalized; the forecasted PV power output data of the model have to be anti-normalized before calculating the normalized root-mean-square error (nRMSE), mean absolute error (MAE), and mean bias error (MBE)
The results showed that the model could forecast the PV power generation accurately in various seasonalities of Malaysian weather conditions
Summary
Especially electrical energy, plays a key role in a country’s development; it improves the living standard of people. Yang et al [17] proposed an autoregressive method, with an exogenous input (ARX)-based spatio-temporal (ST) model, in order to improve the accuracy of the developed PV output power forecasting technique. These time-series models have limitations because they require stationary data-sets [18]. The paper is organized as follows: Section 2 presents a brief description of the methods applied to forecast the PV power generation including real PV plant data collection and analysis; Section 3 indicates the performance metrics to evaluate the forecasting models; and Section 4 discusses the results of the proposed model including comparison and validations.
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