Abstract
The common analog approach and ensemble methods in photovoltaic (PV) power forecasting are based on the forecasts from several numerical weather prediction (NWP) models. These may be not applicable to the very-short-term PV power forecasting, since forecasts based on NWP models are reliable in horizons longer than six hours. In this paper, a methodology for one-hour-ahead PV power forecasting is proposed. Instead of the NWP models, the persistence method is applied in the analog approach to produce meteorological forecasts. The historical data with meteorological predictions similar to the target forecast hour are identified to train the forecast model. Then, the feed forward neural networks (FNNs) act as the base predictors of the neural network ensemble method to replace the NWP-based PV power prediction methods. The forecast results produced by the FNNs are combined by the random forest (RF) algorithm. The performance of the proposed method is evaluated on a real grid-connected PV plant located in Southeast China. Results show that the proposed method outperforms six benchmark models: the persistence model, the support vector regression (SVR) model, the linear regression model, the RF model, the gradient boosting model, and XGBoost model. The improvements reach up to over 40% for the standard error metrics.
Highlights
Fast growing penetration of solar energy has introduced noticeable challenges to the operation of the electric grid due to the dynamic nature and intermittency of solar power
Motivated by the widely used analog methods based on the numerical weather prediction (NWP) data, we propose a new analog approach which is suitable for one-hour-ahead PV power forecasting, without using the NWP data
The proposed analog plus network ensemble (NNE) method is compared with six benchmark methods for PV power forecasting, namely, the persistence model, the support vector regression (SVR) model, the linear regression model, the random forest (RF) model, the gradient boosting model, and XGBoost model
Summary
Fast growing penetration of solar energy has introduced noticeable challenges to the operation of the electric grid due to the dynamic nature and intermittency of solar power. The uncertainty and fluctuations of solar photovoltaic (PV) power must be handled properly. An accurate power forecasting model can reduce the impacts of PV power variability on the grid, improve system reliability and power quality, and promote large-scale PV power penetration [1,2]. A good number of studies have been conducted to forecast PV power at different temporal scales. We focus on the very-short-term PV power forecasting. It is used for forecasting ramps and frequent fluctuations in energy production to ensure unit commitment, scheduling, and dispatching of electrical power [1,4]
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