Abstract
The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 min. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.
Highlights
Solar energy is a completely free cost and accessible source of energy that has proven to be one of the cleanest and most abundant renewable energy sources
From the figures, when the prediction step is larger than 60 min, it can be seen that the linear regression results will have a large regression error
We have proposed a new recurrent neural network (RNN)-based short-term method for forecasting PV power
Summary
Solar energy is a completely free cost and accessible source of energy that has proven to be one of the cleanest and most abundant renewable energy sources. The access of a large number of PV power plants to randomness and intermittency will seriously affect the stable operation of the entire power system [2]. It should be mentioned that PV power forecasting is an important factor in the power system to solve the problem of solar PV power plant optimization planning and modeling. The research in [3] indicates that an accurate forecasting of PV power becomes crucial to improve the power system stability and to ensure an optimal unit commitment and economic dispatch
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